Datasets:

aviaefrat
/

cryptonite

- {"default": {"description": "We study negotiation dialogues where two agents, a buyer and a seller,\nnegotiate over the price of an time for sale. We collected a dataset of more\nthan 6K negotiation dialogues over multiple categories of products scraped from Craigslist.\nOur goal is to develop an agent that negotiates with humans through such conversations.\nThe challenge is to handle both the negotiation strategy and the rich language for bargaining.\n", "citation": "@misc{he2018decoupling,\n title={Decoupling Strategy and Generation in Negotiation Dialogues},\n author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang},\n year={2018},\n eprint={1808.09637},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://stanfordnlp.github.io/cocoa/", "license": "", "features": {"agent_info": {"feature": {"Bottomline": {"dtype": "string", "id": null, "_type": "Value"}, "Role": {"dtype": "string", "id": null, "_type": "Value"}, "Target": {"dtype": "float32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "agent_turn": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "dialogue_acts": {"feature": {"intent": {"dtype": "string", "id": null, "_type": "Value"}, "price": {"dtype": "float32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "utterance": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "items": {"feature": {"Category": {"dtype": "string", "id": null, "_type": "Value"}, "Images": {"dtype": "string", "id": null, "_type": "Value"}, "Price": {"dtype": "float32", "id": null, "_type": "Value"}, "Description": {"dtype": "string", "id": null, "_type": "Value"}, "Title": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "craigslist_bargains", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8538836, "num_examples": 5247, "dataset_name": "craigslist_bargains"}, "test": {"name": "test", "num_bytes": 1353933, "num_examples": 838, "dataset_name": "craigslist_bargains"}, "validation": {"name": "validation", "num_bytes": 966032, "num_examples": 597, "dataset_name": "craigslist_bargains"}}, "download_checksums": {"https://worksheets.codalab.org/rest/bundles/0xd34bbbc5fb3b4fccbd19e10756ca8dd7/contents/blob/parsed.json": {"num_bytes": 20148723, "checksum": "34033ff87565b9fc9eb0efe867e9d3e32456dbe1528cd1683f94a84b09f66ace"}, "https://worksheets.codalab.org/rest/bundles/0x15c4160b43d44ee3a8386cca98da138c/contents/blob/parsed.json": {"num_bytes": 2287054, "checksum": "03b35dc18bd90d87dac46893ac4db8ab3eed51786d192975be68d3bab38e306e"}, "https://worksheets.codalab.org/rest/bundles/0x54d325bbcfb2463583995725ed8ca42b/contents/blob/": {"num_bytes": 2937841, "checksum": "c802f15f80ea3066d429375393319d7234daacbd6a26a6ad5afd0ad78a2f7736"}}, "download_size": 25373618, "post_processing_size": null, "dataset_size": 10858801, "size_in_bytes": 36232419}, "cryptonite": {"description": "Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language\nCurrent NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, \na large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each \nexample in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving \nrequires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a \nchallenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite \nis a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on \npar with the accuracy of a rule-based clue solver (8.6%).\n", "citation": "@misc{efrat2021cryptonite,\n title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language}, \n author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},\n year={2021},\n eprint={2103.01242},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/aviaefrat/cryptonite", "license": "", "features": {"clue": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "enumeration": {"dtype": "string", "id": null, "_type": "Value"}, "publisher": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "int64", "id": null, "_type": "Value"}, "quick": {"dtype": "bool", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "new_dataset", "config_name": "cryptonite", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 52228597, "num_examples": 470804, "dataset_name": "new_dataset"}, "validation": {"name": "validation", "num_bytes": 2901768, "num_examples": 26156, "dataset_name": "new_dataset"}, "test": {"name": "test", "num_bytes": 2908275, "num_examples": 26157, "dataset_name": "new_dataset"}}, "download_checksums": {"https://github.com/aviaefrat/cryptonite/blob/main/data/cryptonite-official-split.zip?raw=true": {"num_bytes": 21615952, "checksum": "c0022977effc68b3f0e72bfe639263d5aaaa36f11287f3ec018e8db42dadb410"}}, "download_size": 21615952, "post_processing_size": null, "dataset_size": 58038640, "size_in_bytes": 79654592}}