TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/P17-1147.pdf
Summary
Existing reading comprehension benchmarks tend to stress only one difficulty, and models quickly approach human scores on them. TriviaQA is a new dataset meant to test several difficulties together: compositional questions, wording that differs from the evidence text, and reasoning that spans more than one sentence. The authors scraped about 95K question-answer pairs written by trivia hobbyists on 14 quiz websites. Evidence was collected only afterwards, so the questions were never written to fit a given passage. The evidence comes from Bing Web search results and from Wikipedia pages of entities found by the TAGME linker, and only documents that contain the answer string were kept. Because collection is automatic, the documents act as distant supervision. A manual check found that the answer-containing documents really support the answer in more than 75% of cases. Two baselines, a feature-based classifier and the BiDAF neural model, score far below humans. That gap makes TriviaQA a demanding benchmark for reading comprehension and open-domain QA.
Key points
- Size: over 650K question-answer-evidence triples, built from 95,956 QA pairs (40,478 distinct answers) and 662,659 evidence documents. Each question has about six documents on average.
- Two domains: in the Web domain, each question-document pair is a separate example (about 650K). In the Wikipedia domain, a question's documents are pooled into one example (about 78K, with 1.8 documents each on average).
- Accuracy: the classifier reaches about 23% and BiDAF about 40%, compared with roughly 80% (79.7%) for humans. The same BiDAF model scores 68% on SQuAD.
- Difficulty: about three times as many questions need multi-sentence reasoning as in SQuAD. Annotated questions mention 1.77 entities on average.
- Verified subset: 1,975 question-document-answer triples were checked by humans to confirm the document holds every fact needed. Data and code are at nlp.cs.washington.edu/triviaqa.
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Cite
@inproceedings{joshi2017triviaqa,
title = {{TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension}},
author = {Joshi, Mandar and Choi, Eunsol and Weld, Daniel and Zettlemoyer, Luke},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
year = {2017},
doi = {10.18653/v1/p17-1147},
eprint = {1705.03551},
archivePrefix = {arXiv},
url = {https://doi.org/10.18653/v1/p17-1147}
}References
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