Datasets:
Tasks:
Text Classification
Size:
1M<n<10M
| annotations_creators: | |
| - expert-generated | |
| language: | |
| - en | |
| - de | |
| - fr | |
| language_creators: | |
| - found | |
| license: [] | |
| multilinguality: | |
| - multilingual | |
| pretty_name: XNLI Code-Mixed Corpus | |
| 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: de_ec | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: int64 | |
| # class_label: | |
| # names: | |
| # '0': spoken | |
| # '1': written | |
| splits: | |
| - name: train | |
| num_bytes: 576 | |
| num_examples: 2490 | |
| - name: test | |
| num_bytes: 194139776 | |
| num_examples: 1610549 | |
| - config_name: de_ml | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: int64 | |
| # class_label: | |
| # names: | |
| # '0': spoken | |
| # '1': written | |
| splits: | |
| - name: train | |
| num_bytes: 576 | |
| num_examples: 2490 | |
| - name: test | |
| num_bytes: 87040 | |
| num_examples: 332326 | |
| - config_name: fr_ec | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: int64 | |
| # class_label: | |
| # names: | |
| # '0': spoken | |
| # '1': written | |
| splits: | |
| - name: train | |
| num_bytes: 576 | |
| num_examples: 2490 | |
| - name: test | |
| num_bytes: 564416 | |
| num_examples: 2562631 | |
| - config_name: fr_ml | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: int64 | |
| # class_label: | |
| # names: | |
| # '0': spoken | |
| # '1': written | |
| splits: | |
| - name: train | |
| num_bytes: 576 | |
| num_examples: 2490 | |
| - name: test | |
| num_bytes: 361472 | |
| num_examples: 1259159 | |
| download_size: 1376728 | |
| dataset_size: 1376704 | |
| # Dataset Card for XNLI Code-Mixed Corpus | |
| ## 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 | |
| - de-ec | |
| - train (English, German, French monolingual): | |
| - test (German-English code-mixed by Equivalence Constraint Theory): | |
| - de-ml: | |
| - train (English, German, French monolingual): | |
| - test (German-English code-mixed by Matrix Language Theory): | |
| - fr-ec | |
| - train (English, German, French monolingual): | |
| - test (French-English code-mixed by Equivalence Constraint Theory): | |
| - fr-ml: | |
| - train (English, German, French monolingual): | |
| - test (French-English code-mixed by Matrix Language Theory): | |
| ### Other Statistics | |
| #### Average Sentence Length | |
| - German | |
| - train: | |
| - test: | |
| - French | |
| - train: | |
| - test: | |
| #### Label Split | |
| - train: | |
| - 0: | |
| - 1: | |
| - test: | |
| - 0: | |
| - 1: | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| Using the XNLI Parallel Corpus, we generated a code-mixed corpus using CodeMixed Text Generator. | |
| 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 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 |