# Incorporating Temporal Information in Entailment Graph Mining

arXiv: https://arxiv.org/abs/2109.09412 · HTML: https://mlsanity.com/paper/2109.09412/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference](https://mlsanity.com/paper/1906.01393/) (2019)
- [Temporal and Aspectual Entailment](https://mlsanity.com/paper/1904.01297/) (2019)
- [Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations](https://mlsanity.com/paper/2006.11824/) (2020)

## Built on by (most cited first, 7 of 7)

- [AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph](https://mlsanity.com/paper/2311.09174/) (2023)
- [AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation](https://mlsanity.com/paper/2402.10646/) (2024)
- [Entailment Graph Learning with Textual Entailment and Soft Transitivity](https://mlsanity.com/paper/2204.03286/) (2022)
- [Smoothing Entailment Graphs with Language Models](https://mlsanity.com/paper/2208.00318/) (2022)
- [Blindness to Modality Helps Entailment Graph Mining](https://mlsanity.com/paper/2109.10227/) (2021)
- [Zero-shot Cross-Linguistic Learning of Event Semantics](https://mlsanity.com/paper/2207.02356/) (2022)
- [From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation](https://mlsanity.com/paper/2306.04170/) (2023)

## Abstract (arXiv)

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose rightarrow play, while avoiding the pitfall of learning non-entailments such as win notrightarrow lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.