Temporal Reasoning on Implicit Events from Distant Supervision

By Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, Dan Roth. 2020

95 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Time-Aware Language Models as Temporal Knowledge Bases (2021, background): “Additionally, we did not test the ability to model temporal expressions such as “before” or “during”, and we did not investigate temporal commonsense (e.g., Zhou et al. 2019), temporal ordering (e.g., Ning et al. 2020) events (e.g., Zhou et al. 2021).”
Reasoning with Transformer-based Models: Deep Learning, but Shallow Reasoning (2021, background): “In the same spirit, Zhou et al. [2021] developed TRACIE, a temporal reasoning textual entailment dataset that asks whether one event preceded another one.”