metadata

annotations_creators:
  - expert-generated
language:
  - en
  - de
  - fr
language_creators:
  - found
license: []
multilinguality:
  - multilingual
pretty_name: XNLI Code-Mixed Corpus (Sampled)
size_categories:
  - 1M<n<10M
source_datasets:
  - extended|xnli
tags:
  - mode classification
  - aligned
  - code-mixed
task_categories:
  - text-classification
task_ids: []
dataset_info:
  - config_name: monolingual
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 317164
        num_examples: 2490
      - name: test
        num_bytes: 641496
        num_examples: 5007
    download_size: 891209
    dataset_size: 958660
  - config_name: de_ec
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 317164
        num_examples: 2490
      - name: test
        num_bytes: 1136549
        num_examples: 14543
    download_size: 1298619
    dataset_size: 1453713
  - config_name: de_ml
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 317164
        num_examples: 2490
      - name: test
        num_bytes: 1068937
        num_examples: 12750
    download_size: 1248962
    dataset_size: 1386101
  - config_name: fr_ec
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 317164
        num_examples: 2490
      - name: test
        num_bytes: 1520429
        num_examples: 18653
    download_size: 1644995
    dataset_size: 1837593
  - config_name: fr_ml
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 317164
        num_examples: 2490
      - name: test
        num_bytes: 1544539
        num_examples: 17381
    download_size: 1682885
    dataset_size: 1861703
download_size: 891209
dataset_size: 958660

Dataset Card for XNLI Code-Mixed Corpus (Sampled)

Dataset Description

Homepage:
Repository:
Paper:
Leaderboard:
Point of Contact:

Dataset Summary

Supported Tasks and Leaderboards

Binary mode classification (spoken vs written)

Languages

English
German
French
German-English code-mixed by Equivalence Constraint Theory
German-English code-mixed by Matrix Language Theory
French-English code-mixed by Equivalence Constraint Theory
German-English code-mixed by Matrix Language Theory

Dataset Structure

Data Instances

{ 'text': "And he said , Mama , I 'm home", 'label': 0 }

Data Fields

text: sentence
label: binary label of text (0: spoken 1: written)

Data Splits

monolingual
- train (English, German, French monolingual): 2490
- test (English, German, French monolingual): 5007
de_ec
- train (English, German, French monolingual): 2490
- test (German-English code-mixed by Equivalence Constraint Theory): 14543
de_ml
- train (English, German, French monolingual): 2490
- test (German-English code-mixed by Matrix Language Theory): 12750
fr_ec
- train (English, German, French monolingual): 2490
- test (French-English code-mixed by Equivalence Constraint Theory): 18653
fr_ml
- train (English, German, French monolingual): 2490
- test (French-English code-mixed by Matrix Language Theory): 17381

Other Statistics

Average Sentence Length

monolingual
- train: 19.18714859437751
- test: 19.321150389454765
de_ec
- train: 19.18714859437751
- test: 11.24314103004882
de_ml
- train: 19.18714859437751
- test: 12.159450980392156
fr_ec
- train: 19.18714859437751
- test: 12.26526564091567
fr_ml
- train: 19.18714859437751
- test: 13.486968528853346

Label Split

monolingual
- train
  - 0: 498
  - 1: 1992
- test
  - 0: 1002
  - 1: 4005
de_ec
- train
  - 0: 498
  - 1: 1992
- test
  - 0: 2777
  - 1: 11766
de_ml
- train
  - 0: 498
  - 1: 1992
- test
  - 0: 2329
  - 1: 10421
fr_ec
- train
  - 0: 498
  - 1: 1992
- test
  - 0: 3322
  - 1: 15331
fr_ml
- train
  - 0: 498
  - 1: 1992
- test
  - 0: 2788
  - 1: 14593

Dataset Creation

Curation Rationale

Using the XNLI Parallel Corpus, we generated a code-mixed corpus using CodeMixed Text Generator, and sampled a maximum of 30 sentences per original English sentence.

The XNLI Parallel Corpus is available here: https://huggingface.co/datasets/nanakonoda/xnli_parallel It was created from the XNLI corpus. More information is available in the datacard for the XNLI Parallel Corpus.

Here is the link and citation for the original CodeMixed Text Generator paper. https://github.com/microsoft/CodeMixed-Text-Generator

@inproceedings{rizvi-etal-2021-gcm,
    title = "{GCM}: A Toolkit for Generating Synthetic Code-mixed Text",
    author = "Rizvi, Mohd Sanad Zaki  and
      Srinivasan, Anirudh  and
      Ganu, Tanuja  and
      Choudhury, Monojit  and
      Sitaram, Sunayana",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-demos.24",
    pages = "205--211",
    abstract = "Code-mixing is common in multilingual communities around the world, and processing it is challenging due to the lack of labeled and unlabeled data. We describe a tool that can automatically generate code-mixed data given parallel data in two languages. We implement two linguistic theories of code-mixing, the Equivalence Constraint theory and the Matrix Language theory to generate all possible code-mixed sentences in the language-pair, followed by sampling of the generated data to generate natural code-mixed sentences. The toolkit provides three modes: a batch mode, an interactive library mode and a web-interface to address the needs of researchers, linguists and language experts. The toolkit can be used to generate unlabeled text data for pre-trained models, as well as visualize linguistic theories of code-mixing. We plan to release the toolkit as open source and extend it by adding more implementations of linguistic theories, visualization techniques and better sampling techniques. We expect that the release of this toolkit will help facilitate more research in code-mixing in diverse language pairs.",
}

Source Data

XNLI Code-Mixed Corpus https://huggingface.co/datasets/nanakonoda/xnli_cm

XNLI Parallel Corpus https://huggingface.co/datasets/nanakonoda/xnli_parallel

Original Source Data

XNLI Parallel Corpus was created using the XNLI Corpus. https://github.com/facebookresearch/XNLI

Here is the citation for the original XNLI paper.

@InProceedings{conneau2018xnli,
  author = "Conneau, Alexis
        and Rinott, Ruty
        and Lample, Guillaume
        and Williams, Adina
        and Bowman, Samuel R.
        and Schwenk, Holger
        and Stoyanov, Veselin",
  title = "XNLI: Evaluating Cross-lingual Sentence Representations",
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  location = "Brussels, Belgium",
}

Initial Data Collection and Normalization

We removed all punctuation from the XNLI Parallel Corpus except apostrophes.

Who are the source language producers?

N/A

Annotations

Annotation process

N/A

Who are the annotators?

N/A

Personal and Sensitive Information

N/A

Considerations for Using the Data

Social Impact of Dataset

N/A

Discussion of Biases

N/A

Other Known Limitations

N/A

Additional Information

Dataset Curators

N/A

Licensing Information

N/A

Citation Information

Contributions

N/A