Base_JSON/prefixN/json/nodalida/2021.nodalida-main.4.json

{
    "paper_id": "2021",
    "header": {
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        "date_generated": "2023-01-19T14:31:57.130072Z"
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    "title": "Large-Scale Contextualised Language Modelling for Norwegian",
    "authors": [
        {
            "first": "Andrey",
            "middle": [],
            "last": "Kutuzov",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "University of Oslo",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Jeremy",
            "middle": [],
            "last": "Barnes",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "University of Oslo",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Erik",
            "middle": [],
            "last": "Velldal",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "University of Oslo",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Lilja",
            "middle": [],
            "last": "\u00d8vrelid",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "University of Oslo",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Stephan",
            "middle": [],
            "last": "Oepen",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "University of Oslo",
                "location": {}
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    "abstract": "We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training. This paper introduces the first largescale monolingual language models for Norwegian, based on both the ELMo and BERT frameworks. In addition to detailing the training process, we present contrastive benchmark results on a suite of NLP tasks for Norwegian.",
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        "abstract": [
            {
                "text": "We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training. This paper introduces the first largescale monolingual language models for Norwegian, based on both the ELMo and BERT frameworks. In addition to detailing the training process, we present contrastive benchmark results on a suite of NLP tasks for Norwegian.",
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                "section": "Abstract",
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                "text": "In this work, we present NorLM, an ongoing community initiative and emerging collection of largescale contextualised language models for Norwegian. We here introduce the NorELMo and Nor-BERT models, that have been trained on around two billion tokens of running Norwegian text. We describe the training procedure and compare these models with the multilingual mBERT model (Devlin et al., 2019) , as well as an additional Norwegian BERT model developed contemporaneously, with some interesting differences in training data and setup. We report results over a number of Norwegian benchmark datasets, addressing a broad range of diverse NLP tasks: part-of-speech tagging, negation resolution, sentence-level and fine-grained sentiment analysis and named entity recognition (NER).",
                "cite_spans": [
                    {
                        "start": 372,
                        "end": 393,
                        "text": "(Devlin et al., 2019)",
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "All the models are publicly available for download from the Nordic Language Processing Lab-oratory (NLPL) Vectors Repository 1 with a CC BY 4.0 license. They are also accessible locally, together with the training and supporting software, on the two national superclusters Puhti and Saga, in Finland and Norway, respectively, which are available to university NLP research groups in Northern Europe through the Nordic Language Processing Laboratory (NLPL). 2 The NorBERT model is in addition served via the Huggingface Transformers model hub. 3 NorLM is a joint effort of the projects EOSC-Nordic (European Open Science Cloud) and SANT (Sentiment Analysis for Norwegian), coordinated by the Language Technology Group (LTG) at the University of Oslo. The goal of this work is to provide these models and supporting tools for researchers and developers in Natural Language Processing (NLP) for the Norwegian language. We do so in the hope of facilitating scientific experimentation with and practical applications of state-of-the-art NLP architectures, as well as to enable others to develop their own large-scale models, for example for domain-or applicationspecific tasks, language variants, or even other languages than Norwegian. Under the auspices of the NLPL use case in EOSC-Nordic, we are also coordinating with colleagues in Denmark, Finland, and Sweden on a collection of large contextualised language models for the Nordic languages, including language variants or related groups of languages, as linguistically or technologically appropriate.",
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                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "Bokm\u00e5l and Nynorsk There are two official standards for written Norwegian; Bokm\u00e5l, the main variety, and Nynorsk, used by 10-15% of the Norwegian population. Norwegian language legislation specifies that minimally 25% of the written public service information should be in Nynorsk. While the two varieties are closely related, there can also be relatively large differences lexically (though often with a large degree of overlap on the character-level still). Several previous studies have indicated that joint modeling of Bokm\u00e5l and Nynorsk works well for many NLP tasks, like tagging and parsing and NER (J\u00f8rgensen et al., 2020) . The contextualised language models presented in this paper are therefore trained jointly on both varieties, but with the minority variant Nynorsk represented by comparatively less data than Bokm\u00e5l (reflecting the natural usage).",
                "cite_spans": [
                    {
                        "start": 606,
                        "end": 630,
                        "text": "(J\u00f8rgensen et al., 2020)",
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                ],
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "Datasets For all our models presented below, we used the following training corpora:",
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "1. Norsk Aviskorpus (NAK), a collection of Norwegian news texts 4 (both Bokm\u00e5l and Nynorsk) from 1998 to 2019; 1.7 billion words;",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "2. Bokm\u00e5l Wikipedia dump from September 2020; 160 million words;",
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                "eq_spans": [],
                "section": "Background",
                "sec_num": "2"
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                "text": "3. Nynorsk Wikipedia dump from September 2020; 40 million words.",
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "The corpora contain ordered sentences (which is important for BERT-like models, because one of their training tasks is next sentence prediction). In total, our training corpus comprises about two billion (1,907,072,909) word tokens in 203 million (202, 802, 665) sentences.",
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                        "text": "(202,",
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                ],
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                "section": "Background",
                "sec_num": "2"
            },
            {
                "text": "We conducted the following pre-processing steps:",
                "cite_spans": [],
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                "section": "Background",
                "sec_num": "2"
            },
            {
                "text": "1. Wikipedia texts were extracted from the dumps using the segment wiki script from the Gensim project (\u0158eh\u016f\u0159ek and Sojka, 2010).",
                "cite_spans": [],
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "2. For the news texts from Norwegian Aviskorpus, we performed de-tokenization and conversion to UTF-8 encoding, where required.",
                "cite_spans": [],
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                "section": "Background",
                "sec_num": "2"
            },
            {
                "text": "3. The resulting corpus was sentencesegmented using Stanza (Qi et al., 2020) . We left blank lines between documents (and 3 Prerequisites: software and computing",
                "cite_spans": [
                    {
                        "start": 59,
                        "end": 76,
                        "text": "(Qi et al., 2020)",
                        "ref_id": "BIBREF19"
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "Developing very large contextualised language models is no small challenge, both in terms of engineering sophistication and computing demands. Training ELMo-and in particular BERT-like models presupposes access to specialised hardwaregraphical processing units (GPUs) -over extended periods of time. Compared to the original work at Google or to our sister initiative at the National Library of Norway (see below), our two billion tokens in Norwegian training data can be characterised as moderate in size. Nevertheless, training a single NorBERT model requires close to one full year of GPU utilisation, which through parallelization over multiple compute nodes, each featuring four GPUs, could be completed in about three weeks of wall clock time. At this scale, premium software efficiency and effective parallelization are prerequisites, not only to allow repeated incremental training and evaluation cycles to complete in practical intervals, but equally so for cost-efficient utilisation of scarce, shared computing resources and, ultimately, a shred of environmental sustainability.",
                "cite_spans": [],
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                "section": "Background",
                "sec_num": "2"
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                "text": "To prepare the NorLM software environment, we have teamed up with support staff at the Norwegian national e-infrastructure provider, Uninett Sigma2, and developed a fully automated and modularised installation procedure using the Easy-Build framework (https://easybuild.io). All necessary tools are compiled from source with the right set of hardware-specific optimizations and platform-specific optimised libraries for basic linear algebra ('math kernels') and communication across multiple compute nodes.",
                "cite_spans": [],
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "This approach to software provisioning makes it possible to (largely) automatically create fully parallel training and experimentation environments on multiple computing infrastructures -in our work to date two national HPC superclusters, in Norway and Finland, but in principle just as much any suitable local GPU cluster. In our view, making available both a ready-to-run software environment on Nordic national e-infrastructures, where university research groups typically can gain nocost access, coupled with the recipe for recreating the environment on other HPC systems, may contribute to 'democratising' large-scale NLP research; if nothing else, it eliminates dependency on commercial cloud computing services.",
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                "section": "Background",
                "sec_num": "2"
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            {
                "text": "Large-scale deep learning language models (LM) are important components of current NLP systems. They are often based on BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) and other contextualised architectures. A number of language-specific initiatives have in recent years released monolingual versions of these models for a number of languages (Fares et al., 2017; Kutuzov and Kuzmenko, 2017; Virtanen et al., 2019; de Vries et al., 2019; Ul\u010dar and Robnik-\u0160ikonja, 2020; Koutsikakis et al., 2020; Nguyen and Nguyen, 2020; Farahani et al., 2020; Malmsten et al., 2020) . For our purposes, the most important such previous training effort is that of Virtanen et al. (2019) on creating a BERT model for Finnish -FinBERT 5as our training setup for creating NorBERT builds heavily on this; see Section 6 for more details.",
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                        "start": 183,
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                        "text": "(Devlin et al., 2019)",
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                        "text": "(Fares et al., 2017;",
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                        "text": "Kutuzov and Kuzmenko, 2017;",
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                        "text": "Virtanen et al., 2019;",
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                        "text": "de Vries et al., 2019;",
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                        "text": "Ul\u010dar and Robnik-\u0160ikonja, 2020;",
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                        "start": 507,
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                        "text": "Koutsikakis et al., 2020;",
                        "ref_id": "BIBREF6"
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                        "start": 533,
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                        "text": "Nguyen and Nguyen, 2020;",
                        "ref_id": "BIBREF14"
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                        "start": 558,
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                        "text": "Farahani et al., 2020;",
                        "ref_id": "BIBREF3"
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                        "text": "Malmsten et al., 2020)",
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                "section": "Related work",
                "sec_num": "4"
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                "text": "Many low-resource languages do not have dedicated monolingual large-scale language models, and instead resort to using a multilingual model, such as Google's multilingual BERT model -mBERT -which was trained on data that also included Norwegian. Up until the release of the models described in the current paper, mBERT was the only BERT-instance that could be used for Norwegian. 6 Another widely used architecture for contextualised LMs is Embeddings From Language Models or ELMo (Peters et al., 2018) . The ElmoFor-ManyLangs initiative (Che et al., 2018) trained and released monolingual ELMo models for a wide range of different languages, including Norwegian (with separate models for Bokm\u00e5l and Nynorsk). However, these models were trained on very modestly sized corpora of 20 million words for each language (randomly sampled from Wikipedia dumps and Common Crawl data).",
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                "section": "Related work",
                "sec_num": "4"
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                "text": "In a parallel effort to that of the current paper, the AI Lab of the National Library of Norway, through their Norwegian Transformer Model (No-TraM) project, has released a Norwegian BERT (Base, cased) model dubbed NB-BERT (Kummervold et al., 2021 ). 7 The model is trained on the Colossal Norwegian Corpus, reported to comprise close to 18,5 billion words (109.1 GB of text).",
                "cite_spans": [
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                        "text": "(Kummervold et al., 2021",
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                "sec_num": "4"
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                "text": "In raw numbers, this is about ten times more than the corpus we use for training the NorLM models. However, the vast majority of this is from OCR'ed historical sources, which is bound to introduce at least some noise. In Section 7 below, we demonstrate that in some NLP tasks, a language model trained on less (but arguably cleaner) data can outperform a model trained on larger but noisy corpora.",
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                "section": "Related work",
                "sec_num": "4"
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                "text": "NorELMo is a set of bidirectional recurrent ELMo language models trained from scratch on the Norwegian corpus described in Section 1. They can be used as a source of contextualised token representations for various Norwegian natural language processing tasks. As we show below, in many cases, they present a viable alternative to Transformer-based models like BERT. Their performance is often only marginally lower, while the compute time required to adapt the model to the task at hand can be an order of magnitude less on identical hardware.",
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                "section": "NorELMo",
                "sec_num": "5"
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                "text": "Currently we present two models, with more following in the future:",
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                "section": "NorELMo",
                "sec_num": "5"
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                "text": "1. NorELMo 30 : 30,000 most frequent words in the vocabulary 2. NorELMo 100 : 100,000 most frequent words in the vocabulary Note that independent of the vocabulary size, both NorELMo 30 and NorELMo 100 can process arbitrary word tokens, due to the ELMo architecture (where the first CNN layer converts input strings to non-contextual word embeddings). Thus, the size of the vocabulary controls only the number of words used as targets for the language modelling task in the course of training. Supposedly, the model with a larger vocabulary is more effective in treating less frequent words at the cost of being less effective with more frequent words.",
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                "section": "NorELMo",
                "sec_num": "5"
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                "text": "Each model was trained for 3 epochs with batch size 192. We employed a version of the original ELMo training code from Peters et al. (2018) updated to work better with the recent TensorFlow versions. All the hyperparameters were left at their default values, except the LSTM dimensionality reduced to 2,048 from the default 4,096 (in our experience, this rarely influences performance). Training of each model took about 100 hours on four NVIDIA P100 GPUs.",
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                        "text": "Peters et al. (2018)",
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                "section": "NorELMo",
                "sec_num": "5"
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            {
                "text": "These are the first ELMo models for Norwegian trained on a large corpus. As has already been mentioned, the Norwegian ELMo models from the ElmoForManyLangs project (Che et al., 2018) were trained on very small corpora samples and seriously under-perform on semanticrelated NLP tasks, although they can yield impressive results on POS tagging and syntactic parsing (Zeman et al., 2018) . In addition, they were trained with custom code modifications and can be used only with the custom ElmoForManyLangs library. On the other hand, our NorELMo models are fully compatible both with the original ELMo implementation by Peters et al. (2018) and with the more modern simple elmo Python library provided by us. 8 The vocabularies are published together with the models. For different tasks, different models can be better, as we show below. The published packages contain both TensorFlow checkpoints (for possible fine-tuning, if need be) and model files in the standard Hierarchical Data Format (HDF5) for easier inference usage. In addition, we have setup ELMoViz, a demo web service to explore Norwegian ELMo models. 9",
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                "section": "NorELMo",
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                "text": "Our NorBERT model is trained from scratch for Norwegian, and can be used in exactly the same way as any other BERT-like model. The NorBERT training setup heavily builds on prior work on Fin-BERT conducted at the University of Turku (Virtanen et al., 2019).",
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                "section": "NorBERT",
                "sec_num": "6"
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                "text": "NorBERT features a custom WordPiece vocabulary which is case-sensitive and includes accented characters. It has much better coverage of Norwegian words than the mBERT model or NB-BERT (which uses the same vocabulary as mBERT). This is clearly seen on the example of the tokenization performed by both for the Norwe-gian sentence 'Denne gjengen h\u00e5per at de sammen skal bidra til\u00e5 gi kvinnefotballen i Kristiansand et lenge etterlengtet l\u00f8ft'",
                "cite_spans": [],
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                "section": "NorBERT",
                "sec_num": "6"
            },
            {
                "text": "\u2022 mBERT/NB-BERT: 'Denne g ##jeng ##en h ##\u00e5 ##per at de sammen skal bid ##ra til a gi k ##vinne ##fo ##t ##ball ##en i Kristiansand et lenge etter ##len ##gte ##t l ##\u00f8 ##ft'",
                "cite_spans": [],
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                "section": "NorBERT",
                "sec_num": "6"
            },
            {
                "text": "\u2022 NorBERT: 'Denne gjengen h\u00e5per at de sammen skal bidra til\u00e5 gi kvinne ##fotball ##en i Kristiansand et lenge etterl ##engt ##et l\u00f8ft'",
                "cite_spans": [],
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                "section": "NorBERT",
                "sec_num": "6"
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                "text": "NorBERT tokenization splits the sentence into pieces which much better reflect the real Norwegian words and morphemes (cf. 'k vinne fo t ball en' versus 'kvinne fotball en'). We believe this to be extremely important for more linguistically-oriented studies, where it is critical to deal with words, not with arbitrarily fragmented pieces (even if they are well-performing in practical tasks).",
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                "section": "NorBERT",
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                "text": "The vocabulary for the model is of size 30,000. It is much less than the 120,000 of mBERT, but it is compensated by these entities being almost exclusively Norwegian. The vocabulary was generated from raw text, without, e.g., separating punctuation from word tokens. This means one can feed raw text into NorBERT.",
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                "section": "NorBERT",
                "sec_num": "6"
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                "text": "For the vocabulary generation, we used the Sen-tencePiece algorithm (Kudo, 2018) and Tokenizers library. 10 The resulting Tokenizers model was converted to the standard BERT WordPiece format. The final vocabulary contains several thousand unused wordpiece slots which can be filled in with task-specific lexical entries for further finetuning by future NorBERT users.",
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                        "text": "(Kudo, 2018)",
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                "section": "NorBERT",
                "sec_num": "6"
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                "text": "NorBERT corresponds in its configuration to the Google's Bert-Base Cased for English, with 12 layers and hidden size 768 (Devlin et al., 2019) . We used the standard masked language modeling and next sentence prediction losses with the LAMB optimizer (You et al., 2020) . The model was trained on the Norwegian academic HPC system called Saga. Most of the time the training process was distributed across 4 compute nodes and 16 NVIDIA P100 GPUs. Overall, it took approximately 3 weeks (more than 500 hours). 2019, we employed the BERT implementation by NVIDIA 11 , which allows fast multi-node and multi-GPU training.",
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                "section": "Training technicalities",
                "sec_num": "6.1"
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                "text": "We made minor changes to this code, mostly to adapt it to the newer TensorFlow versions. All these patches and the utilities we used at the preprocessing, training and evaluation stages are published in our GitHub repository. 12 Instructions to reproduce the training setup with the EasyBuild software build and installation framework are also available. 13",
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                "section": "Training technicalities",
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            },
            {
                "text": "Phase 1 (training with maximum sequence length of 128) was done with batch size 48 and global batch size 48*16=768. Since one global batch contains 768 sentences, approximately 265,000 training steps constitute 1 epoch (one pass over the whole corpus). We have done 3 epochs: 795,000 training steps.",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Training workflow",
                "sec_num": "6.2"
            },
            {
                "text": "Phase 2 (training with maximum sequence length of 512) was done with batch size 8 and global batch size 8*16=128. We aimed at mimicking the original BERT in that at Phase 2 the model should see about 1/9 of the number of sentences seen during Phase 1. Thus, we needed about 68 million sentences, which at the global batch size of 128 boils down to 531,000 training steps more.",
                "cite_spans": [],
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                "section": "Training workflow",
                "sec_num": "6.2"
            },
            {
                "text": "The loss plots are shown in Figure 1 (the training was on pause on December 25 and 26, since we were solving problems with mixed precision 11 https://github.com/NVIDIA/ DeepLearningExamples/tree/master/ TensorFlow/LanguageModeling/BERT, version 20.06.08",
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                "ref_spans": [
                    {
                        "start": 28,
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                        "text": "Figure 1",
                        "ref_id": "FIGREF0"
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                ],
                "eq_spans": [],
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                "sec_num": "6.2"
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                "text": "12 https://github.com/ltgoslo/NorBERT 13 http://wiki.nlpl.eu/index.php/Eosc/ pretraining/nvidia ",
                "cite_spans": [],
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                "section": "Training workflow",
                "sec_num": "6.2"
            },
            {
                "text": "This section presents benchmark results across a range of different tasks. We compare NorELMO and NorBERT to both mBERT and to the recently released NB-BERT model described in Section 4. Where applicable, we show separate evaluation results for Bokm\u00e5l and Nynorsk. Below we first provide an overview of the different tasks and the corresponding classifiers that we train, before turning to discuss the results.",
                "cite_spans": [],
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                "section": "Evaluation",
                "sec_num": "7"
            },
            {
                "text": "We start by briefly describing each task and associated dataset, in addition to the architectures we use. The sentence counts for the different datasets and their train, dev. and test splits are provided in Table 1 .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 207,
                        "end": 214,
                        "text": "Table 1",
                        "ref_id": "TABREF1"
                    }
                ],
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                "section": "Task descriptions",
                "sec_num": "7.1"
            },
            {
                "text": "Part-of-speech tagging The Norwegian Dependency Treebank (NDT) (Solberg et al., 2014) in-cludes annotation of POS tags for both Bokm\u00e5l and Nynorsk. NDT has also been converted to the Universal Dependencies format (\u00d8vrelid and Hohle, 2016; and this is the version we are using here (for UD 2.7) for predicting UPOS tags. We use a typical sequence labelling approach with the BERT models, adding a linear layer after the final token representations and taking the softmax to get token predictions. We fine-tune all parameters for 20 epochs, using a learning rate of 2e-5, a training batch size of 8, max length of 256, and keep the best model on the development set. ELMo models were not fine-tuned, following the recommendations from Peters et al. (2019) . Instead we trained a simple neural classifier (a feed forward network with one hidden layer of size 128, ReLU non-linear activation function and dropout), using ELMo token embeddings as features. The random seed has been kept fixed all the time. Models are evaluated on accuracy.",
                "cite_spans": [
                    {
                        "start": 213,
                        "end": 238,
                        "text": "(\u00d8vrelid and Hohle, 2016;",
                        "ref_id": "BIBREF15"
                    },
                    {
                        "start": 733,
                        "end": 753,
                        "text": "Peters et al. (2019)",
                        "ref_id": "BIBREF18"
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                ],
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                "section": "Task descriptions",
                "sec_num": "7.1"
            },
            {
                "text": "Named entity recognition The NorNE 14 dataset annotates the UD-version of NDT with a rich set of entity types (J\u00f8rgensen et al., 2020) . The evaluation metrics here is 'strict' micro F 1 , requiring both the correct entity type and exact match of boundary surface string. We predict 8 entity types: Person (PER), Organisation (ORG), Location (LOC), Geo-political entity, with a locative sense (GPE-LOC), Geo-political entity, with an organisation sense (GPE-ORG), Product (PROD), Event (EVT), Nominals derived from names (DRV). The evaluation is done using the code for the SemEval'13 Task 9 15 .",
                "cite_spans": [
                    {
                        "start": 110,
                        "end": 134,
                        "text": "(J\u00f8rgensen et al., 2020)",
                        "ref_id": "BIBREF5"
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                ],
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                "section": "Task descriptions",
                "sec_num": "7.1"
            },
            {
                "text": "We cast the named entity recognition problem as a sequence labelling task, using a BIO label encoding. For the BERT-based models, we solve it by fine-tuning the pre-trained model on the NorNE dataset for 20 epochs with early stopping and batch size 32. The resulting model is applied to the test set.",
                "cite_spans": [],
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                "section": "Task descriptions",
                "sec_num": "7.1"
            },
            {
                "text": "For ELMo models, we infer contextualised token embeddings (averaged representations across all 3 layers) for all words. Then, these token embeddings are fed to a neural classifier with dropout, identical to the one we used for POS tagging earlier. This classifier is also trained for 20 epochs with early stopping and batch size 32.",
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                "section": "Task descriptions",
                "sec_num": "7.1"
            },
            {
                "text": "Fine-grained sentiment analysis NoReC fine is a dataset 16 comprising a subset of the Norwegian Review Corpus (NoReC; Velldal et al., 2018) annotated for sentiment holders, targets, expressions, and polarity, as well as the relationships between them (\u00d8vrelid et al., 2020) . We here cast the problem as a graph prediction task and train a graph parser (Dozat and Manning, 2018; Kurtz et al., 2020) to predict sentiment graphs. The parser creates token-level representations which is the concatenation of a word embedding, POS tag embedding, lemma embedding, and character embedding created by a character-based LSTM. We further augment these representations with contextualised embeddings from each model. Models are trained for 100 epochs, keeping the best model on development F 1 . For span extraction (holders, targets, expressions), we evaluate token-level F 1 , and the common Targeted F 1 metric, which requires correctly extracting a target (strict) and its polarity. We also evaluate Labelled and Unlabelled F 1 , which correspond to Labelled and Unlabelled Attachment in dependency parsing. Finally, we evaluate on Sentiment Graph F 1 (SF 1 ) and Non-polar Sentiment Graph F 1 (NSF 1 . SF 1 requires predicting all elements (holder, target, expression, polarity) and their relationships (NSF 1 removes the polarity). A true positive is defined as an exact match at graph-level, weighting the overlap in predicted and gold spans for each element, averaged across all three spans. For precision we weight the number of correctly predicted tokens divided by the total number of predicted tokens (for recall, we divide instead by the number of gold tokens). We allow for empty holders and targets.",
                "cite_spans": [
                    {
                        "start": 118,
                        "end": 139,
                        "text": "Velldal et al., 2018)",
                        "ref_id": "BIBREF23"
                    },
                    {
                        "start": 251,
                        "end": 273,
                        "text": "(\u00d8vrelid et al., 2020)",
                        "ref_id": null
                    },
                    {
                        "start": 353,
                        "end": 378,
                        "text": "(Dozat and Manning, 2018;",
                        "ref_id": "BIBREF2"
                    },
                    {
                        "start": 379,
                        "end": 398,
                        "text": "Kurtz et al., 2020)",
                        "ref_id": "BIBREF9"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Task descriptions",
                "sec_num": "7.1"
            },
            {
                "text": "We further evaluate on the task of sentence-level binary (positive or negative) polarity classification, using labels that we derive from NoReC fine described above. We create the dataset for this by aggregating the fine-grained annotations to the sentence-level, removing sentences with mixed or no sentiment. The resulting dataset, NoReC sentence , is made publicly available. 17 For the BERT models, we use the [CLS] embedding of the last layer as a representation for the sentence and pass this to a softmax layer for classification. We fine-tune the models in the same way as for the POS tagging task, training the models for 20 epochs and keeping the model that performs best on the development data. For ELMo models, we used a BiLSTM with global max pooling, taking ELMo token embeddings from the top layer as an input. The evaluation metric is macro F 1 .",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Sentence-level binary sentiment classification",
                "sec_num": null
            },
            {
                "text": "Negation detection Finally, the NoReC fine dataset has recently been annotated with negation cues and their corresponding in-sentence scopes (Maehlum et al., 2021) . The resulting dataset is dubbed NoReC neg . 18 We use the same graph-based modeling approach as described for fine-grained sentiment above. We evaluate on the same metrics as in the *SEM 2012 shared task (Morante and Blanco, 2012) : cue-level F 1 (CUE), scope token F 1 over individual tokens (ST), and the combined full negation F 1 (FN).",
                "cite_spans": [
                    {
                        "start": 141,
                        "end": 163,
                        "text": "(Maehlum et al., 2021)",
                        "ref_id": null
                    },
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                        "start": 210,
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                        "text": "18",
                        "ref_id": null
                    },
                    {
                        "start": 370,
                        "end": 396,
                        "text": "(Morante and Blanco, 2012)",
                        "ref_id": "BIBREF13"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Sentence-level binary sentiment classification",
                "sec_num": null
            },
            {
                "text": "We present the results for the various benchmarking tasks below.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Results",
                "sec_num": "7.2"
            },
            {
                "text": "POS tagging As can be seen from Table 2 , Nor-BERT outperforms mBERT on both tasks: on POS tagging for Bokm\u00e5l by 5 percentage points and 1 percentage point for Nynorsk. NorBERT is almost on par with NB-BERT on POS tagging. NorELMo models are outperformed by NB-BERT and Nor-BERT, but are on par with mBERT in POS tagging. Note that their adaptation to the tasks (extracting token embeddings and learning a classifier) takes 30x less time than with the BERT models.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 32,
                        "end": 39,
                        "text": "Table 2",
                        "ref_id": "TABREF3"
                    }
                ],
                "eq_spans": [],
                "section": "Results",
                "sec_num": "7.2"
            },
            {
                "text": "18 https://github.com/ltgoslo/norec_neg Table 3 : NER evaluation scores (micro F 1 ) of the NorLM models on the NorNE test set in comparison with other large pre-trained models for Norwegian. Running time is given in minutes for the Bokm\u00e5l part (on 1 NVIDIA P100 GPU).",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 40,
                        "end": 47,
                        "text": "Table 3",
                        "ref_id": null
                    }
                ],
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                "section": "Results",
                "sec_num": "7.2"
            },
            {
                "text": "See Figure 2 for the examples of training dynamics of the Nynorsk model. Table 3 shows the performance on the NER task. NB-BERT is the best on both Bokm\u00e5l and Nynorsk, closely followed by NorBERT. Unsurprisingly, mBERT falls behind all the models trained for Norwegian, when evaluated on Bokm\u00e5l data. With Nynorsk, it manages to outperform NorELMo. Bokm\u00e5l is presumably dominant in the training corpora of both. However, in the course of fine-tuning, mBERT seems to be able to adapt to the specifics of Nynorsk. Since our ELMo setup did not include the fine-tuning step, the NorELMo models' adaptation abilities were limited by what can be learned from contextualised token embeddings produced by a frozen model. Still, when used on the data more similar to the training corpus (Bokm\u00e5l), ELMo achieves competitive results even without any fine-tuning.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 4,
                        "end": 12,
                        "text": "Figure 2",
                        "ref_id": null
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                        "start": 73,
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                        "ref_id": null
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            {
                "text": "In terms of computational efficiency, the adaptation of ELMo models to this task requires 6x less time than mBERT or NB-BERT and 4x less time than NorBERT. Note also that the NorBERT model takes less time to fine-tune than the NB-BERT model (although the number of epochs was exactly the same), because of a smaller vocabulary, and thus less parameters in the model. Again, in this case an NLP practitioner has a rich spectrum of tools to choose from, depending on whether speed or performance on the downstream task is prioritised. Table 4 shows that NorBERT outperforms mBERT on all metrics and NB-BERT on all but SF 1 , although the differences between NorBERT and NB-BERT are gen- Table 4 : Average score of NorLM models on fine-grained sentiment (5 runs with set random seeds).",
                "cite_spans": [],
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                    {
                        "start": 533,
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                        "text": "Table 4",
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                        "text": "Table 4",
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                ],
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                "section": "Named entity recognition",
                "sec_num": null
            },
            {
                "text": "Bold denotes the best result on each metric.",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Fine-grained sentiment analysis",
                "sec_num": null
            },
            {
                "text": "[1] Span extraction baseline from \u00d8vrelid et al. (2020) , which uses a BiLSTM CRF with pretrained fastText embeddings.",
                "cite_spans": [
                    {
                        "start": 34,
                        "end": 55,
                        "text": "\u00d8vrelid et al. (2020)",
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                "sec_num": null
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            {
                "text": "NorELMo 30 75.0 NorELMo 100 75.0 mBERT 67.7 NB-BERT 83.9 NorBERT 77.1 erally small. On this task the NorELMo models generally outperform mBERT as well. However, unlike in the previous tasks, the running times here are similar for BERT and ELMo models, since no finetuning was applied (the same is true for negation detection). We furthermore compare with the previous best model (\u00d8vrelid et al., 2020) , a span extraction model which uses a single-layer Bidirectional LSTM with Conditional Random Field inference, and an embedding layer initialized with fastText vectors trained on the NoWaC corpus. All approaches using language models outperform the previous baseline by a large margin on the span extraction tasks. 19 NorBERT, in particular, achieves improvements of 20.6 percentage points on Holder F 1 (24.9 and 25.8 on Target and Exp. F 1 , respectively). Table 5 shows that NorBERT outperforms mBERT by 9.4 percentage points on sentiment analysis. However, it seems that in binary sentiment classification the sheer amount of training data starts to show its benefits, and NB-BERT outperforms NorBERT by 6.8 points. NorELMo models outperform mBERT by 7.3 points. Figure 2 shows the training dynamics of the models. Table 6 we can see that mBERT gives the best overall results, followed by NorBERT and NorELMo 100 . NB-BERT and NorELMo 30 perform worse than the others on Scope token F 1 (ST) and full negation F 1 (FN), while all models perform similarly at cue-level F 1 (CUE). We hypothesise that the structural similarity of negation across many of the pretraining languages gives mBERT an advantage, but it is still surprising that it outperforms NB-BERT and Nor-BERT.",
                "cite_spans": [
                    {
                        "start": 379,
                        "end": 401,
                        "text": "(\u00d8vrelid et al., 2020)",
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                        "text": "Table 5",
                        "ref_id": "TABREF6"
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                        "start": 1170,
                        "end": 1178,
                        "text": "Figure 2",
                        "ref_id": null
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                        "start": 1222,
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                        "text": "Table 6",
                        "ref_id": "TABREF7"
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                ],
                "eq_spans": [],
                "section": "Model F 1",
                "sec_num": null
            },
            {
                "text": "In the future, separate Bokm\u00e5l and Nynorsk BERT models are planned, and we further expect to train and evaluate models with a higher number of epochs over the training corpus. While we plan to develop additional monolingual Norwegian models based on other contextualised LM architectures Figure 2 : Per-epoch performance on training and development data for two of the tasks. Left: accuracy for POS tagging (Norwegian Nynorsk). Right: F 1 for binary sentiment classification.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 288,
                        "end": 296,
                        "text": "Figure 2",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Future plans",
                "sec_num": "8"
            },
            {
                "text": "beyond BERT and ELMo, we would also be interested to explore the usefulness of multilingual models restricted to Scandinavian languages. Further streamlining of the benchmarking process, in terms of both data access and computation of metrics, is something we also want to address in future work.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Future plans",
                "sec_num": "8"
            },
            {
                "text": "In addition, the ready availability of a highly optimised software stack on multiple HPC systems (published as part of NorLM) may contribute to other researchers developing very large contextualised language models for additional languages or language variants, e.g. domain-or application-specific sub-corpora. We hope that more pre-trained NLP models for Norwegian from both academy and industry will be openly released, making it possible to study the interplay between training corpora sizes, hyperparameters, pre-preprocessing decisions and performance in different tasks. At the same time, given the resource demands and sustainability issues related to training such models, we believe it will be important to coordinate efforts and we hope to collaborate closely with other players moving forward.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Future plans",
                "sec_num": "8"
            },
            {
                "text": "This paper has described the first outcomes of NorLM, an initiative coordinated by the Language Technology Group at the University of Oslo seeking to provide Norwegian (and Nordic) largescale contextualised language models, while simultaneously focusing on maintaining a re-usable software environment for model development on national and Nordic HPC infrastructure. We have here described the training and testing of NorELMo and NorBERT -the first large-scale monolingual LMs for Norwegian. We have benchmarked the models across a wide array of Norwegian NLP tasks, also comparing to the multilingual mBERT model and another large-scale LM for Norwegian developed in parallel work, NB-BERT, trained on large amounts of text from historical sources. The results show that while the monolingual models tend to yield better results, which particular model ranks first varies across tasks. This underscores the importance of building an ecosystem of diversified models, accompanied by systematic benchmarking.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Summary",
                "sec_num": "9"
            },
            {
                "text": "http://vectors.nlpl.eu/repository 2 http://www.nlpl.eu 3 https://huggingface.co/ltgoslo/ norbert",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://www.nb.no/sprakbanken/ ressurskatalog/oai-nb-no-sbr-4/ sections in the case of Wikipedia) so that the 'next sentence prediction' task of BERT does not span between documents.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://github.com/TurkuNLP/FinBERT 6 A BERT model trained on Norwegian data was published at https://github.com/botxo/nordic_bert in the beginning of 2020. However, the vocabulary of this model seems to be broken, and to the best of our knowledge nobody has achieved any meaningful results with it.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://github.com/NBAiLab/notram",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://pypi.org/project/simple-elmo/ 9 http://vectors.nlpl.eu/explore/ embeddings/en/contextual/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://github.com/huggingface/ tokenizers",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://github.com/ltgoslo/norne 15 https://github.com/davidsbatista/ NER-Evaluation",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://github.com/ltgoslo/norec_fine 17 https://github.com/ltgoslo/norec_ sentence",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "\u00d8vrelid et al. (2020) only perform span extraction. Therefore, it is not possible to compare the other metrics.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "The NorLM resources are being developed on the Norwegian national super-computing services operated by UNINETT Sigma2, the National Infrastructure for High Performance Computing and Data Storage in Norway, as well as on the Finnish national supercomputing facilities operated by the CSC IT Center for Science. Software provisioning was financially supported through the European EOSC-Nordic project; data preparation and evaluation were supported by the SANT project (Sentiment Analysis for Norwegian Text), funded by the Research Council of Norway (grant number 270908). We are indebted to all funding agencies involved, the University of Oslo, and the Norwegian tax payer.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgements",
                "sec_num": null
            }
        ],
        "bib_entries": {
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            "FIGREF0": {
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                "text": "NorBERT loss plots at the Phase 1 (left) and Phase 2 (right).Similar to Virtanen et al."
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            "TABREF1": {
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                "content": "<table/>",
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                "text": "Number of sentences in the training, development, and test splits in the datasets used for the evaluation tasks."
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                "content": "<table><tr><td>: Evaluation scores of the NorLM models</td></tr><tr><td>on the POS tagging of Bokm\u00e5l (BM) and Nynorsk</td></tr><tr><td>(NN) test sets in comparison with other large pre-</td></tr><tr><td>trained models for Norwegian. Running times in</td></tr><tr><td>minutes are given for Bokm\u00e5l.</td></tr></table>",
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            },
            "TABREF6": {
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                "content": "<table><tr><td>Model</td><td>CUE</td><td>ST</td><td colspan=\"2\">FN Time</td></tr><tr><td>NorELMo 30</td><td colspan=\"3\">91.7 80.6 63.8</td><td>428</td></tr><tr><td colspan=\"4\">NorELMo 100 92.2 81.3 65.5</td><td>407</td></tr><tr><td>mBERT</td><td colspan=\"3\">92.8 84.0 65.9</td><td>353</td></tr><tr><td>NB-BERT</td><td colspan=\"3\">92.4 83.1 63.5</td><td>342</td></tr><tr><td>NorBERT</td><td colspan=\"3\">92.1 83.6 65.5</td><td>426</td></tr></table>",
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                "text": "F 1 scores for the different LMs models on the binary sentiment classification test set."
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            "TABREF7": {
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                "content": "<table><tr><td>: Results of our negation parser, augment-</td></tr><tr><td>ing the features with token representations from</td></tr><tr><td>each language model. The results are averaged</td></tr><tr><td>over 5 runs.</td></tr></table>",
                "type_str": "table",
                "html": null,
                "text": ""
            }
        }
    }
}