{ "mixed": { "description": "Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.\nWe present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.\n", "citation": "@article{sun2019investigating,\n title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},\n author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},\n journal={Transactions of the Association for Computational Linguistics},\n year={2020},\n url={https://arxiv.org/abs/1904.09679v3}\n}\n", "homepage": "https://github.com/nlpdata/c3", "license": "", "features": { "documents": { "feature": { "dtype": "string", "_type": "Value" }, "_type": "Sequence" }, "document_id": { "dtype": "string", "_type": "Value" }, "questions": { "feature": { "question": { "dtype": "string", "_type": "Value" }, "answer": { "dtype": "string", "_type": "Value" }, "choice": { "feature": { "dtype": "string", "_type": "Value" }, "_type": "Sequence" } }, "_type": "Sequence" } }, "builder_name": "parquet", "dataset_name": "c3", "config_name": "mixed", "version": { "version_str": "1.0.0", "major": 1, "minor": 0, "patch": 0 }, "splits": { "train": { "name": "train", "num_bytes": 2710473, "num_examples": 3138, "dataset_name": null }, "test": { "name": "test", "num_bytes": 891579, "num_examples": 1045, "dataset_name": null }, "validation": { "name": "validation", "num_bytes": 910759, "num_examples": 1046, "dataset_name": null } }, "download_size": 3183780, "dataset_size": 4512811, "size_in_bytes": 7696591 }, "dialog": { "description": "Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.\nWe present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.\n", "citation": "@article{sun2019investigating,\n title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},\n author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},\n journal={Transactions of the Association for Computational Linguistics},\n year={2020},\n url={https://arxiv.org/abs/1904.09679v3}\n}\n", "homepage": "https://github.com/nlpdata/c3", "license": "", "features": { "documents": { "feature": { "dtype": "string", "_type": "Value" }, "_type": "Sequence" }, "document_id": { "dtype": "string", "_type": "Value" }, "questions": { "feature": { "question": { "dtype": "string", "_type": "Value" }, "answer": { "dtype": "string", "_type": "Value" }, "choice": { "feature": { "dtype": "string", "_type": "Value" }, "_type": "Sequence" } }, "_type": "Sequence" } }, "builder_name": "parquet", "dataset_name": "c3", "config_name": "dialog", "version": { "version_str": "1.0.0", "major": 1, "minor": 0, "patch": 0 }, "splits": { "train": { "name": "train", "num_bytes": 2039779, "num_examples": 4885, "dataset_name": null }, "test": { "name": "test", "num_bytes": 646955, "num_examples": 1627, "dataset_name": null }, "validation": { "name": "validation", "num_bytes": 611106, "num_examples": 1628, "dataset_name": null } }, "download_size": 2073256, "dataset_size": 3297840, "size_in_bytes": 5371096 } }