Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation. However, these represent a significant domain shift from existing entailment datasets, and models underperform as a result. We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim, and a minimal subset of evidence sentences that support each subclaim. To support this, we propose an automatic claim decomposition strategy using GPT-3.5 which we show is also effective at improving entailment models’ performance on multiple datasets at test time. Finally, we show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.
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Fool Me Twice: Entailment from Wikipedia Gamification
We release FOOLMETWICE (FM2 for short),
a large dataset of challenging entailment pairs
collected through a fun multi-player game.
Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using “shortcuts” compared to other entailment datasets. Players are
presented with two tasks. The first task asks
the player to write a plausible claim based on
the evidence from a Wikipedia page. The second one shows two plausible claims written by
other players, one of which is false, and the
goal is to identify it before the time runs out.
Players “pay” to see clues retrieved from the
evidence pool: the more evidence the player
needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and
results in higher quality data for the entailment
and evidence retrieval tasks. We open source
the dataset and game code.
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- Award ID(s):
- 1822494
- NSF-PAR ID:
- 10309825
- Date Published:
- Journal Name:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2021.naacl-main.32
