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Datasets:
indic_glue

Tasks:
Text Classification
Token Classification
Multiple Choice
Sub-tasks:
topic-classification
natural-language-inference
sentiment-analysis
Languages:
Assamese
Bengali
English
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
other
Source Datasets:
extended|other
Tags:
discourse-mode-classification
paraphrase-identification
cross-lingual-similarity
headline-classification
License:
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metadata
annotations_creators:
  - other
language:
  - as
  - bn
  - en
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
language_creators:
  - found
license:
  - other
multilinguality:
  - multilingual
pretty_name: IndicGLUE
size_categories:
  - 100K<n<1M
source_datasets:
  - extended|other
task_categories:
  - text-classification
  - token-classification
  - multiple-choice
task_ids:
  - topic-classification
  - natural-language-inference
  - sentiment-analysis
  - semantic-similarity-scoring
  - text-classification-other-headline-classification
  - text-classification-other-paraphrase-identification
  - text-classification-other-cross-lingual-similarity
  - text-classification-other-discourse-mode-classification
  - named-entity-recognition
  - multiple-choice-qa
paperswithcode_id: null

Dataset Card for "indic_glue"

Table of Contents

Dataset Description

Dataset Summary

IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.

The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, we construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. We call converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3 Indian languages by AI4Bharat.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

actsa-sc.te

  • Size of downloaded dataset files: 0.36 MB
  • Size of the generated dataset: 1.63 MB
  • Total amount of disk used: 1.99 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "label": 0,
    "text": "\"ప్రయాణాల్లో ఉన్నవారికోసం బస్ స్టేషన్లు, రైల్వే స్టేషన్లలో పల్స్పోలియో బూతులను ఏర్పాటు చేసి చిన్నారులకు పోలియో చుక్కలు వేసేలా ఏర..."
}

bbca.hi

  • Size of downloaded dataset files: 5.50 MB
  • Size of the generated dataset: 26.35 MB
  • Total amount of disk used: 31.85 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "label": "pakistan",
    "text": "\"नेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर पर सरकार द्वारा लगाए प्रतिबंधों के समर्थन या विरोध में अपने विचार व्यक्त करते है..."
}

copa.en

  • Size of downloaded dataset files: 0.72 MB
  • Size of the generated dataset: 0.11 MB
  • Total amount of disk used: 0.83 MB

An example of 'validation' looks as follows.

{
    "choice1": "I swept the floor in the unoccupied room.",
    "choice2": "I shut off the light in the unoccupied room.",
    "label": 1,
    "premise": "I wanted to conserve energy.",
    "question": "effect"
}

copa.gu

  • Size of downloaded dataset files: 0.72 MB
  • Size of the generated dataset: 0.22 MB
  • Total amount of disk used: 0.94 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "choice1": "\"સ્ત્રી જાણતી હતી કે તેનો મિત્ર મુશ્કેલ સમયમાંથી પસાર થઈ રહ્યો છે.\"...",
    "choice2": "\"મહિલાને લાગ્યું કે તેના મિત્રએ તેની દયાળુ લાભ લીધો છે.\"...",
    "label": 0,
    "premise": "મહિલાએ તેના મિત્રની મુશ્કેલ વર્તન સહન કરી.",
    "question": "cause"
}

copa.hi

  • Size of downloaded dataset files: 0.72 MB
  • Size of the generated dataset: 0.22 MB
  • Total amount of disk used: 0.94 MB

An example of 'validation' looks as follows.

{
    "choice1": "मैंने उसका प्रस्ताव ठुकरा दिया।",
    "choice2": "उन्होंने मुझे उत्पाद खरीदने के लिए राजी किया।",
    "label": 0,
    "premise": "मैंने सेल्समैन की पिच पर शक किया।",
    "question": "effect"
}

Data Fields

The data fields are the same among all splits.

actsa-sc.te

  • text: a string feature.
  • label: a classification label, with possible values including positive (0), negative (1).

bbca.hi

  • label: a string feature.
  • text: a string feature.

copa.en

  • premise: a string feature.
  • choice1: a string feature.
  • choice2: a string feature.
  • question: a string feature.
  • label: a int32 feature.

copa.gu

  • premise: a string feature.
  • choice1: a string feature.
  • choice2: a string feature.
  • question: a string feature.
  • label: a int32 feature.

copa.hi

  • premise: a string feature.
  • choice1: a string feature.
  • choice2: a string feature.
  • question: a string feature.
  • label: a int32 feature.

Data Splits

actsa-sc.te

train validation test
actsa-sc.te 4328 541 541

bbca.hi

train test
bbca.hi 3467 866

copa.en

train validation test
copa.en 400 100 500

copa.gu

train validation test
copa.gu 362 88 448

copa.hi

train validation test
copa.hi 362 88 449

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{kakwani-etal-2020-indicnlpsuite,
    title = "{I}ndic{NLPS}uite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for {I}ndian Languages",
    author = "Kakwani, Divyanshu  and
      Kunchukuttan, Anoop  and
      Golla, Satish  and
      N.C., Gokul  and
      Bhattacharyya, Avik  and
      Khapra, Mitesh M.  and
      Kumar, Pratyush",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.findings-emnlp.445",
    doi = "10.18653/v1/2020.findings-emnlp.445",
    pages = "4948--4961",
}

@inproceedings{Levesque2011TheWS,
title={The Winograd Schema Challenge},
author={H. Levesque and E. Davis and L. Morgenstern},
booktitle={KR},
year={2011}
}

Contributions

Thanks to @sumanthd17 for adding this dataset.