Existing works on temporal reasoning among events described in text focus on modeling relationships between explicitly mentioned events and do not handle event end time effectively. However, human readers can infer from natural language text many implicit events that help them better understand the situation and, consequently, better reason about time. This work proposes a new crowd-sourced dataset, TRACIE, which evaluates systems ' understanding of implicit events - events that are not mentioned explicitly in the text but can be inferred from it. This is done via textual entailment instances querying both start and end times of events. We show that TRACIE is challenging for state-of-the-art language models . Our proposed model, SymTime, exploits distant supervision signals from the text itself and reasons over events' start time and duration to infer events' end time points. We show that our approach improves over baseline language models, gaining 5\% on the i.i.d. split and 9\% on an out-of-distribution test split . Our approach is also general to other annotation schemes, gaining 2\%-8 \% on MATRES, an extrinsic temporal relation benchmark.
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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Existing works on temporal reasoning among events described in text focus on modeling relationships between explicitly mentioned events and do not handle event end time effectively. However, human readers can infer from natural language text many implicit events that help them better understand the situation and, consequently, better reason about time. This work proposes a new crowd-sourced dataset, TRACIE, which evaluates systems ' understanding of implicit events -
We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events --
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is done via textual entailment instances querying both start and end times of events. We show that TRACIE is challenging for
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
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language models . Our proposed model, SymTime,
models struggle when predicting temporal relationships between implicit and explicit events. To address this, we propose a neuro-symbolic temporal reasoning model, SYMTIME, which
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the text itself and reasons over events' start time and duration to infer events' end time points. We show that our approach improves over baseline language models, gaining
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
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on the i.i.d. split and 9\% on an out-of-distribution test split
, and by 11\% in a zero prior knowledge training setting
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is also general to other annotation schemes, gaining 2\%-8
also generalizes to other temporal reasoning tasks, as evidenced by a gain of 1\%-9
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extrinsic temporal relation
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