# Effective Distant Supervision for Temporal Relation Extraction

arXiv: https://arxiv.org/abs/2010.12755 · HTML: https://mlsanity.com/paper/2010.12755/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts](https://mlsanity.com/paper/2210.16865/) (2022)
- [Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction](https://mlsanity.com/paper/2210.04992/) (2022)
- [Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning](https://mlsanity.com/paper/2305.14970/) (2023)
- [Linguistic representations for fewer-shot relation extraction across domains](https://mlsanity.com/paper/2307.03823/) (2023)
- [Transformer-Based Temporal Information Extraction and Application: A Review](https://mlsanity.com/paper/2504.07470/) (2025)
- [WISTERIA: Weak Implicit Signal-based Temporal Relation Extraction with Attention](https://mlsanity.com/paper/2603.23319/) (2026)
- [Looking for the Bottleneck in Fine-grained Temporal Relation Classification](https://mlsanity.com/paper/2604.24620/) (2026)

## Abstract (arXiv)

A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more. We present a method of automatically collecting distantly-supervised examples of temporal relations. We scrape and automatically label event pairs where the temporal relations are made explicit in text, then mask out those explicit cues, forcing a model trained on this data to learn other signals. We demonstrate that a pre-trained Transformer model is able to transfer from the weakly labeled examples to human-annotated benchmarks in both zero-shot and few-shot settings, and that the masking scheme is important in improving generalization.