Temporal Reasoning on Implicit Events from Distant Supervision

• URL: http://arxiv.org/abs/2010.12753v2
• Date: Fri, 7 May 2021 21:07:45 GMT
• Title: Temporal Reasoning on Implicit Events from Distant Supervision
• Authors: Ben Zhou and Kyle Richardson and Qiang Ning and Tushar Khot and Ashish Sabharwal and Dan Roth
• Abstract summary: We propose a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events. We find that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. We propose a neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant supervision signals from large-scale text and uses temporal rules to infer end times.
• Score: 91.20159064951487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it. This introduces a new challenge in temporal reasoning research, where prior work has focused on explicitly mentioned events. Human readers can infer implicit events via commonsense reasoning, resulting in a more comprehensive understanding of the situation and, consequently, better reasoning about time. We find, however, that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. To address this, we propose a neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. SYMTIME outperforms strong baseline systems on TRACIE by 5%, and by 11% in a zero prior knowledge training setting. Our approach also generalizes to other temporal reasoning tasks, as evidenced by a gain of 1%-9% on MATRES, an explicit event benchmark.

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