Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
open-domain-qa
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 7,598 Bytes
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{
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"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n",
"citation": "@inproceedings{ min2020ambigqa,\n title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n booktitle={ EMNLP },\n year={2020}\n}\n",
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"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n",
"citation": "@inproceedings{ min2020ambigqa,\n title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n booktitle={ EMNLP },\n year={2020}\n}\n",
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