# Temporal Reasoning on Implicit Events from Distant Supervision

Source: [Temporal Reasoning on Implicit Events from Distant Supervision](<https://www.sota2.com/research/paper/68fa69e51e209131cea24ad9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:10:38.141Z

Authors: Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, Dan Roth

Published: 2020-10-24T03:12:27.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Binary temporal relation classification on TRACIE, MATRES, and TODAY (test)](<https://www.sota2.com/research/sota/binary-temporal-relation-classification-on-tracie-matres-and-today-test.md>): Binary temporal relation classification; TRACIE, MATRES, and TODAY (test); TRACIE Accuracy: 79.7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.12753>)

[PDF](<https://arxiv.org/pdf/2010.12753.pdf>)