|
{"plain_text": {"description": "Our goal is to build systems that collaborate with people by exchanging\ninformation through natural language and reasoning over structured knowledge\nbase. In the MutualFriend task, two agents, A and B, each have a private\nknowledge base, which contains a list of friends with multiple attributes\n(e.g., name, school, major, etc.). The agents must chat with each other\nto find their unique mutual friend.", "citation": "@inproceedings{he-etal-2017-learning,\n title = \"Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings\",\n author = \"He, He and\n Balakrishnan, Anusha and\n Eric, Mihail and\n Liang, Percy\",\n booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n month = jul,\n year = \"2017\",\n address = \"Vancouver, Canada\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/P17-1162\",\n doi = \"10.18653/v1/P17-1162\",\n pages = \"1766--1776\",\n abstract = \"We study a \textit{symmetric collaborative dialogue} setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.\",\n}\n", "homepage": "https://stanfordnlp.github.io/cocoa/", "license": "Unknown", "features": {"uuid": {"dtype": "string", "id": null, "_type": "Value"}, "scenario_uuid": {"dtype": "string", "id": null, "_type": "Value"}, "scenario_alphas": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "scenario_attributes": {"feature": {"unique": {"dtype": "bool_", "id": null, "_type": "Value"}, "value_type": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "scenario_kbs": {"feature": {"feature": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "agents": {"1": {"dtype": "string", "id": null, "_type": "Value"}, "0": {"dtype": "string", "id": null, "_type": "Value"}}, "outcome_reward": {"dtype": "int32", "id": null, "_type": "Value"}, "events": {"actions": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "start_times": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "data_messages": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "data_selects": {"feature": {"attributes": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "values": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "agents": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "times": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "mutual_friends", "config_name": "plain_text", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 26979472, "num_examples": 8967, "dataset_name": "mutual_friends"}, "test": {"name": "test", "num_bytes": 3327158, "num_examples": 1107, "dataset_name": "mutual_friends"}, "validation": {"name": "validation", "num_bytes": 3267881, "num_examples": 1083, "dataset_name": "mutual_friends"}}, "download_checksums": {"https://worksheets.codalab.org/rest/bundles/0x09c73c9db1134621bcc827689c6c3c61/contents/blob/train.json": {"num_bytes": 33178253, "checksum": "578399bf9339851628bf8b0d96df5386805d87fe9c801c424cd8bd3d476c63e3"}, "https://worksheets.codalab.org/rest/bundles/0x09c73c9db1134621bcc827689c6c3c61/contents/blob/dev.json": {"num_bytes": 4001596, "checksum": "37af10108b353b5508747009a3ecb6bd5203ce66f9d9599b4322b248853aabf7"}, "https://worksheets.codalab.org/rest/bundles/0x09c73c9db1134621bcc827689c6c3c61/contents/blob/test.json": {"num_bytes": 4094729, "checksum": "f8c1117fe9b024554d517da2101d978196d7e28c85ef6683881a39e2e5eb6e3b"}}, "download_size": 41274578, "post_processing_size": null, "dataset_size": 33574511, "size_in_bytes": 74849089}} |
