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TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, and Dan Roth, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.

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Abstract

A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated. However, current machine reading comprehension benchmarks have practically no questions that test temporal phenomena, so systems trained on these benchmarks have no capacity to answer questions such as "what happened before/after [some event]?" We introduce TORQUE, a new English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships. Results show that RoBERTa-large achieves an exact-match score of 51% on the test set of TORQUE, about 30% behind human performance.


Bib Entry

@inproceedings{ning2020torque,
  title = {TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions},
  author = {Ning, Qiang and Wu, Hao and Han, Rujun and Peng, Nanyun and Gardner, Matt and Roth, Dan},
  booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  publisher = {Association for Computational Linguistics},
  pages = {1158--1172},
  slideslive_id = {38938807},
  year = {2020}
}

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