GooAQ: Open Question Answering with Diverse Answer Types

Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hannaneh Hajishirzi, Chris Callison-Burch


Abstract
While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google’s responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmark T5 models on GooAQ and observe that: (a) in line with recent work, LM’s strong performance on GooAQ’s short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as ‘how’ and ‘why’ questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types.
Anthology ID:
2021.findings-emnlp.38
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
421–433
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.38
DOI:
10.18653/v1/2021.findings-emnlp.38
Bibkey:
Cite (ACL):
Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hannaneh Hajishirzi, and Chris Callison-Burch. 2021. GooAQ: Open Question Answering with Diverse Answer Types. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 421–433, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
GooAQ: Open Question Answering with Diverse Answer Types (Khashabi et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.38.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.38.mp4
Code
 allenai/gooaq
Data
GooAQELI5Natural Questions