sharc_modified / dataset_infos.json
{"mod": {"description": "ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. However, it is found that in the ShARC dataset there are multiple spurious patterns that could be exploited by neural models. SharcModified is a new dataset which reduces the patterns identified in the original dataset. To reduce the sensitivity of neural models, for each occurence of an instance conforming to any of the patterns, we automatically construct alternatives where we choose to either replace the current instance with an alternative instance which does not exhibit the pattern; or retain the original instance. The modified ShARC has two versions sharc-mod and history-shuffled. For morre details refer to Appendix A.3 .\n", "citation": "@inproceedings{verma-etal-2020-neural,\n title = \"Neural Conversational {QA}: Learning to Reason vs Exploiting Patterns\",\n author = \"Verma, Nikhil and\n Sharma, Abhishek and\n Madan, Dhiraj and\n Contractor, Danish and\n Kumar, Harshit and\n Joshi, Sachindra\",\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.emnlp-main.589\",\n pages = \"7263--7269\",\n abstract = \"Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the model(s) learn spurious clues/patterns in the data-set. Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.\",\n}\n", "homepage": "https://github.com/nikhilweee/neural-conv-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "utterance_id": {"dtype": "string", "id": null, "_type": "Value"}, "source_url": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}, "history": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "evidence": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sharc_modified", "config_name": "mod", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15138034, "num_examples": 21890, "dataset_name": "sharc_modified"}, "validation": {"name": "validation", "num_bytes": 1474239, "num_examples": 2270, "dataset_name": "sharc_modified"}}, "download_checksums": {"https://raw.githubusercontent.com/nikhilweee/neural-conv-qa/master/datasets/mod_train.json": {"num_bytes": 19301214, "checksum": "84ccbf71dab4c800f63aa9b50f3f911b413cd15c69f042ef93bbe2fdf873c603"}, "https://raw.githubusercontent.com/nikhilweee/neural-conv-qa/master/datasets/mod_dev.json": {"num_bytes": 1896057, "checksum": "426f561e458605bed72580228cc4891ec70be35abbdacec8af027f53d3770992"}}, "download_size": 21197271, "post_processing_size": null, "dataset_size": 16612273, "size_in_bytes": 37809544}, "mod_dev_multi": {"description": "ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. However, it is found that in the ShARC dataset there are multiple spurious patterns that could be exploited by neural models. SharcModified is a new dataset which reduces the patterns identified in the original dataset. To reduce the sensitivity of neural models, for each occurence of an instance conforming to any of the patterns, we automatically construct alternatives where we choose to either replace the current instance with an alternative instance which does not exhibit the pattern; or retain the original instance. The modified ShARC has two versions sharc-mod and history-shuffled. For morre details refer to Appendix A.3 .\n", "citation": "@inproceedings{verma-etal-2020-neural,\n title = \"Neural Conversational {QA}: Learning to Reason vs Exploiting Patterns\",\n author = \"Verma, Nikhil and\n Sharma, Abhishek and\n Madan, Dhiraj and\n Contractor, Danish and\n Kumar, Harshit and\n Joshi, Sachindra\",\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.emnlp-main.589\",\n pages = \"7263--7269\",\n abstract = \"Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the model(s) learn spurious clues/patterns in the data-set. Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.\",\n}\n", "homepage": "https://github.com/nikhilweee/neural-conv-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "utterance_id": {"dtype": "string", "id": null, "_type": "Value"}, "source_url": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}, "history": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "evidence": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "answer": {"dtype": "string", "id": null, "_type": "Value"}, "all_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sharc_modified", "config_name": "mod_dev_multi", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 1553940, "num_examples": 2270, "dataset_name": "sharc_modified"}}, "download_checksums": {"https://raw.githubusercontent.com/nikhilweee/neural-conv-qa/master/datasets/mod_dev_multi.json": {"num_bytes": 2006124, "checksum": "cc49803a0091a05163a23f44c0449048eec63d75b7dc406cab3b48f5cee05e04"}}, "download_size": 2006124, "post_processing_size": null, "dataset_size": 1553940, "size_in_bytes": 3560064}, "history": {"description": "ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. 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Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.\",\n}\n", "homepage": "https://github.com/nikhilweee/neural-conv-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "utterance_id": {"dtype": "string", "id": null, "_type": "Value"}, "source_url": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}, "history": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "evidence": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sharc_modified", "config_name": "history", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15083103, "num_examples": 21890, "dataset_name": "sharc_modified"}, "validation": {"name": "validation", "num_bytes": 1468604, "num_examples": 2270, "dataset_name": "sharc_modified"}}, "download_checksums": {"https://raw.githubusercontent.com/nikhilweee/neural-conv-qa/master/datasets/history_train.json": {"num_bytes": 19246236, "checksum": "89ce72b684b19f16bb7fe57ac1e7f08f4d2fb443a918103ef05b0d9cab37782a"}, "https://raw.githubusercontent.com/nikhilweee/neural-conv-qa/master/datasets/history_dev.json": {"num_bytes": 1890422, "checksum": "4387fa0f0dd9fd68ea42a9f10c808443c20e73696d470fe89aab8856cce076ab"}}, "download_size": 21136658, "post_processing_size": null, "dataset_size": 16551707, "size_in_bytes": 37688365}, "history_dev_multi": {"description": "ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. 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For morre details refer to Appendix A.3 .\n", "citation": "@inproceedings{verma-etal-2020-neural,\n title = \"Neural Conversational {QA}: Learning to Reason vs Exploiting Patterns\",\n author = \"Verma, Nikhil and\n Sharma, Abhishek and\n Madan, Dhiraj and\n Contractor, Danish and\n Kumar, Harshit and\n Joshi, Sachindra\",\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.emnlp-main.589\",\n pages = \"7263--7269\",\n abstract = \"Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the model(s) learn spurious clues/patterns in the data-set. Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.\",\n}\n", "homepage": "https://github.com/nikhilweee/neural-conv-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "utterance_id": {"dtype": "string", "id": null, "_type": "Value"}, "source_url": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}, "history": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "evidence": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "answer": {"dtype": "string", "id": null, "_type": "Value"}, "all_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sharc_modified", "config_name": "history_dev_multi", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 1548305, "num_examples": 2270, "dataset_name": "sharc_modified"}}, "download_checksums": {"https://raw.githubusercontent.com/nikhilweee/neural-conv-qa/master/datasets/history_dev_multi.json": {"num_bytes": 2000489, "checksum": "63b4ea610446b425bd6761d78ce14ea2ccc2a824bcedec22f311068dff768e03"}}, "download_size": 2000489, "post_processing_size": null, "dataset_size": 1548305, "size_in_bytes": 3548794}}