# Temporal Reasoning on Implicit Events from Distant Supervision

arXiv: https://arxiv.org/abs/2010.12753 · HTML: https://mlsanity.com/paper/2010.12753/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 30 of 58)

- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey](https://mlsanity.com/paper/2302.10035/) (2023)
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- [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024)
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- [Stable LM 2 1.6B Technical Report](https://mlsanity.com/paper/2402.17834/) (2024)
- [Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning](https://mlsanity.com/paper/2406.09170/) (2024)
- [Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge](https://mlsanity.com/paper/2102.03315/) (2021)
- [A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://mlsanity.com/paper/2406.11050/) (2024)
- [Test of Time: Instilling Video-Language Models with a Sense of Time](https://mlsanity.com/paper/2301.02074/) (2023)
- [Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning](https://mlsanity.com/paper/2305.16646/) (2023)
- [Benchmarks for Automated Commonsense Reasoning: A Survey](https://mlsanity.com/paper/2302.04752/) (2023)
- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models](https://mlsanity.com/paper/2202.08772/) (2022)
- [Time-R1: Towards Comprehensive Temporal Reasoning in LLMs](https://mlsanity.com/paper/2505.13508/) (2025)
- [DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization](https://mlsanity.com/paper/2203.11239/) (2022)
- [TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation](https://mlsanity.com/paper/2402.05733/) (2024)
- [VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer](https://mlsanity.com/paper/2107.02681/) (2021)
- [There is a Time and Place for Reasoning Beyond the Image](https://mlsanity.com/paper/2203.00758/) (2022)
- [COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective](https://mlsanity.com/paper/2305.05191/) (2023)
- [Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification](https://mlsanity.com/paper/2307.01972/) (2023)
- [Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts](https://mlsanity.com/paper/2210.16865/) (2022)
- [Generic Temporal Reasoning with Differential Analysis and Explanation](https://mlsanity.com/paper/2212.10467/) (2022)
- [BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models](https://mlsanity.com/paper/2404.12494/) (2024)
- [ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events](https://mlsanity.com/paper/2501.03040/) (2025)
- [UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization](https://mlsanity.com/paper/2407.03525/) (2024)
- [Salience-Aware Event Chain Modeling for Narrative Understanding](https://mlsanity.com/paper/2109.10475/) (2021)
- [Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction](https://mlsanity.com/paper/2210.04992/) (2022)
- [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025)
- [Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning](https://mlsanity.com/paper/2305.14970/) (2023)

## Abstract (arXiv)

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.