Datasets:

nirantk
/

hda_nli_hindi

- {"HDA hindi nli": {"description": "This dataset is a recasted version of the Hindi Discourse Analysis Dataset used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi.\n", "citation": " @inproceedings{uppal-etal-2020-two,\n title = \"Two-Step Classification using Recasted Data for Low Resource Settings\",\n author = \"Uppal, Shagun and\n Gupta, Vivek and\n Swaminathan, Avinash and\n Zhang, Haimin and\n Mahata, Debanjan and\n Gosangi, Rakesh and\n Shah, Rajiv Ratn and\n Stent, Amanda\",\n booktitle = \"Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing\",\n month = dec,\n year = \"2020\",\n address = \"Suzhou, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.aacl-main.71\",\n pages = \"706--719\",\n abstract = \"An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.\",\n}\n", "homepage": "https://github.com/midas-research/hindi-nli-data", "license": "\nMIT License\n\nCopyright (c) 2019 MIDAS, IIIT Delhi\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["not-entailment", "entailment"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Argumentative", "Descriptive", "Dialogic", "Informative", "Narrative"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "hda_nli_hindi", "config_name": "HDA hindi nli", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8721972, "num_examples": 31892, "dataset_name": "hda_nli_hindi"}, "validation": {"name": "validation", "num_bytes": 2556118, "num_examples": 9460, "dataset_name": "hda_nli_hindi"}, "test": {"name": "test", "num_bytes": 2646453, "num_examples": 9970, "dataset_name": "hda_nli_hindi"}}, "download_checksums": {"https://raw.githubusercontent.com/avinsit123/hindi-nli-data/master/Textual_Entailment/HDA/HDA_recasted_train.tsv": {"num_bytes": 8470892, "checksum": "4a8937f25005269f8ad513d0ae91d1140180e6c142e439282b56b1a0af7960eb"}, "https://raw.githubusercontent.com/avinsit123/hindi-nli-data/master/Textual_Entailment/HDA/HDA_recasted_test.tsv": {"num_bytes": 2567907, "checksum": "f36a316eccc969c0fce3d66805acbd44dffdf46aa4f6babcd23b52a013ea83ca"}, "https://raw.githubusercontent.com/avinsit123/hindi-nli-data/master/Textual_Entailment/HDA/HDA_recasted_dev.tsv": {"num_bytes": 2480462, "checksum": "e43af6693d236600db111f03dfb7cbef06b4acdf1ec49129e04556590b171fdf"}}, "download_size": 13519261, "post_processing_size": null, "dataset_size": 13924543, "size_in_bytes": 27443804}, "hda nli hindi": {"description": "This dataset is a recasted version of the Hindi Discourse Analysis Dataset used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi.\n", "citation": " @inproceedings{uppal-etal-2020-two,\n title = \"Two-Step Classification using Recasted Data for Low Resource Settings\",\n author = \"Uppal, Shagun and\n Gupta, Vivek and\n Swaminathan, Avinash and\n Zhang, Haimin and\n Mahata, Debanjan and\n Gosangi, Rakesh and\n Shah, Rajiv Ratn and\n Stent, Amanda\",\n booktitle = \"Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing\",\n month = dec,\n year = \"2020\",\n address = \"Suzhou, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.aacl-main.71\",\n pages = \"706--719\",\n abstract = \"An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.\",\n}\n", "homepage": "https://github.com/midas-research/hindi-nli-data", "license": "\nMIT License\n\nCopyright (c) 2019 MIDAS, IIIT Delhi\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["not-entailment", "entailment"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Argumentative", "Descriptive", "Dialogic", "Informative", "Narrative"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "hda_nli_hindi", "config_name": "hda nli hindi", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8721972, "num_examples": 31892, "dataset_name": "hda_nli_hindi"}, "validation": {"name": "validation", "num_bytes": 2556118, "num_examples": 9460, "dataset_name": "hda_nli_hindi"}, "test": {"name": "test", "num_bytes": 2646453, "num_examples": 9970, "dataset_name": "hda_nli_hindi"}}, "download_checksums": {"https://raw.githubusercontent.com/avinsit123/hindi-nli-data/master/Textual_Entailment/HDA/HDA_recasted_train.tsv": {"num_bytes": 8470892, "checksum": "4a8937f25005269f8ad513d0ae91d1140180e6c142e439282b56b1a0af7960eb"}, "https://raw.githubusercontent.com/avinsit123/hindi-nli-data/master/Textual_Entailment/HDA/HDA_recasted_test.tsv": {"num_bytes": 2567907, "checksum": "f36a316eccc969c0fce3d66805acbd44dffdf46aa4f6babcd23b52a013ea83ca"}, "https://raw.githubusercontent.com/avinsit123/hindi-nli-data/master/Textual_Entailment/HDA/HDA_recasted_dev.tsv": {"num_bytes": 2480462, "checksum": "e43af6693d236600db111f03dfb7cbef06b4acdf1ec49129e04556590b171fdf"}}, "download_size": 13519261, "post_processing_size": null, "dataset_size": 13924543, "size_in_bytes": 27443804}}