Datasets:

UCLNLP
/

sharc

- {"sharc": {"description": "ShARC is a Conversational Question Answering dataset focussing on question answering from texts containing rules. The goal is to answer questions by possibly asking follow-up questions first. It is assumed assume that the question is often underspecified, in the sense that the question does not provide enough information to be answered directly. However, an agent can use the supporting rule text to infer what needs to be asked in order to determine the final answer.\n", "citation": "@misc{saeidi2018interpretation,\n title={Interpretation of Natural Language Rules in Conversational Machine Reading},\n author={Marzieh Saeidi and Max Bartolo and Patrick Lewis and Sameer Singh and Tim Rockt\u00e4schel and Mike Sheldon and Guillaume Bouchard and Sebastian Riedel},\n year={2018},\n eprint={1809.01494},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://sharc-data.github.io/index.html", "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"}, "negative_question": {"dtype": "bool_", "id": null, "_type": "Value"}, "negative_scenario": {"dtype": "bool_", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sharc", "config_name": "sharc", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15088577, "num_examples": 21890, "dataset_name": "sharc"}, "validation": {"name": "validation", "num_bytes": 1469172, "num_examples": 2270, "dataset_name": "sharc"}}, "download_checksums": {"https://sharc-data.github.io/data/sharc1-official.zip": {"num_bytes": 5230207, "checksum": "0c185809b807c00df05eeca2c504dc5d6d0ecbf98cf60ea51f7d1e690493b69a"}}, "download_size": 5230207, "post_processing_size": null, "dataset_size": 16557749, "size_in_bytes": 21787956}}