Create README.md

README.md

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+---
+language:
+- multilingual
+license:
+- cc-by-4.0 
+multilinguality:
+- multilingual
+source_datasets:
+- nluplusplus
+task_categories:
+- text-classification
+pretty_name: multi3-nlu
+
+---
+
+# Dataset Card for Multi<sup>3</sup>NLU++
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contact](#contact)
+  
+## Dataset Description
+
+- **Paper:** [arXiv]()
+
+
+### Dataset Summary
+
+Multi<sup>3</sup>NLU++ consists of 3080 utterances per language representing challenges in building multilingual multi-intent multi-domain task-oriented dialogue systems. The domains include banking and hotels. There are 68 unique intents. 
+
+### Supported Tasks and Leaderboards
+
+- multi-label intent detection
+- slot filling
+- cross-lingual language understanding for task-oriented dialogue
+
+### Languages
+
+The dataset covers four language pairs in addition to the source dataset in English: 
+Spanish, Turkish, Marathi, Amharic
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance contains the following features: _text_, _intents_, _uid_, _lang_, and ocassionally _slots_ and _values_
+
+See the [Multi<sup>3</sup>NLU++ corpus viewer](https://huggingface.co/datasets/uoe-nlp/multi3-nlu/viewer/uoe-nlp--multi3-nlu/train) to explore more examples.
+
+An example from the Multi<sup>3</sup>NLU++ looks like the following:
+```
+{
+        "text": "माझे उद्याचे रिझर्वेशन मला रद्द का करता येणार नाही?",
+        "intents": [
+            "why",
+            "booking",
+            "cancel_close_leave_freeze",
+            "wrong_notworking_notshowing"
+        ],
+        "slots": {
+            "date_from": {
+                "text": "उद्याचे",
+                "span": [
+                    5,
+                    12
+                ],
+                "value": {
+                    "day": 16,
+                    "month": 3,
+                    "year": 2022
+                }
+            }
+        },
+        "uid": "hotel_1_1",
+        "lang": "mr"
+
+}
+```
+
+### Data Fields
+
+- 'text': a string containing the utterance for which the intent needs to be detected
+- 'intents': the corresponding intent labels
+- 'uid': unique identifier per language
+- 'lang': the language of the dataset 
+- 'slots': annotation of the span that needs to be extracted for value extraction with its label and _value_
+
+
+### Data Splits
+
+The experiments are done on different k-fold validation setups. The dataset has multiple types of data splits. Please see Section 4 of the paper. 
+
+## Dataset Creation
+
+### Curation Rationale
+Existing task-oriented dialogue datasets are 1) predominantly limited to detecting a single intent, 2) focused on a single domain, and 3) include a small set of slot types. Furthermore, the success of task-oriented dialogue is \textbf{4)} often evaluated on a small set of higher-resource languages (i.e., typically English) which does not test how generalisable systems are to the diverse range of the world's languages.
+Our proposed dataset addresses all these limitations
+
+
+### Source Data
+
+
+#### Initial Data Collection and Normalization
+Please see Section 3 of the paper 
+
+#### Who are the source language producers?
+The source language producers are authors of [NLU++ dataset](https://arxiv.org/abs/2204.13021). The dataset was professionally translated into our chosen four languages. We used Blend Express and Proz.com to recruit these translators. 
+
+
+### Personal and Sensitive Information
+
+None. Names are fictional
+
+
+
+### Discussion of Biases
+
+We have carefully vetted the examples to exclude the problematic examples.
+
+### Other Known Limitations
+The dataset comprises utterances extracted from real dialogues between users and conversational agents as well as synthetic human-authored utterances constructed with the aim of introducing additional combinations of intents and slots. The utterances therefore lack the wider context that would be present in a complete dialogue. As such the dataset cannot be used to evaluate systems with respect to discourse-level phenomena present in dialogue.
+
+## Additional Information
+N/A
+
+### Licensing Information
+
+The dataset is Creative Commons Attribution 4.0 International (cc-by-4.0) 
+
+### Citation Information
+
+Coming soon
+
+### Contact
+[Nikita Moghe](mailto:nikita.moghe@ed.ac.uk) and E[Liane Guillou](mailto:lguillou@ed.ac.uk)
+
+Dataset card based on [Allociné](https://huggingface.co/datasets/allocine)