README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - expert-generated
language:
  - en
  - tw
license:
  - unknown
multilinguality:
  - multilingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - text-scoring
  - semantic-similarity-scoring
paperswithcode_id: null
pretty_name: Yorùbá Wordsim-353
dataset_info:
  features:
    - name: twi1
      dtype: string
    - name: twi2
      dtype: string
    - name: similarity
      dtype: float32
  splits:
    - name: test
      num_bytes: 7285
      num_examples: 274
  download_size: 6141
  dataset_size: 7285

Dataset Card for Yorùbá Wordsim-353

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: -https://www.aclweb.org/anthology/2020.lrec-1.335/
• Repository: https://github.com/ajesujoba/YorubaTwi-Embedding
• Paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
• Leaderboard: -
• Point of Contact: Kwabena Amponsah-Kaakyire

Dataset Summary

A translation of the word pair similarity dataset wordsim-353 to Twi. However, only 274 (out of 353) pairs of words were translated

Supported Tasks and Leaderboards

[More Information Needed]

Languages

Twi (ISO 639-1: tw)

Dataset Structure

Data Instances

An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Twi.

Data Fields

• twi1: the first word of the pair; translation to Twi
• twi2: the second word of the pair; translation to Twi
• similarity: similarity rating according to the English dataset

Data Splits

Only the test data is available

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{alabi-etal-2020-massive,
    title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
    author = "Alabi, Jesujoba  and
      Amponsah-Kaakyire, Kwabena  and
      Adelani, David  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
    pages = "2754--2762",
    abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}

Contributions

Thanks to @dadelani for adding this dataset.