# Entailment Graph Learning with Textual Entailment and Soft Transitivity

arXiv: https://arxiv.org/abs/2204.03286 · HTML: https://mlsanity.com/paper/2204.03286/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A Logic-Driven Framework for Consistency of Neural Models](https://mlsanity.com/paper/1909.00126/) (2019)
- [Probabilistic Models of Relational Implication](https://mlsanity.com/paper/1907.12048/) (2019)
- [Multivalent Entailment Graphs for Question Answering](https://mlsanity.com/paper/2104.07846/) (2021)
- [Language Models for Lexical Inference in Context](https://mlsanity.com/paper/2102.05331/) (2021)
- [Incorporating Temporal Information in Entailment Graph Mining](https://mlsanity.com/paper/2109.09412/) (2021)
- [Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference](https://mlsanity.com/paper/1811.06203/) (2018)
- [Continuous Entailment Patterns for Lexical Inference in Context](https://mlsanity.com/paper/2109.03695/) (2021)

## Built on by (most cited first, 11 of 11)

- [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)
- [Smoothing Entailment Graphs with Language Models](https://mlsanity.com/paper/2208.00318/) (2022)
- [From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization](https://mlsanity.com/paper/2306.03853/) (2023)
- [Language Models Are Poor Learners of Directional Inference](https://mlsanity.com/paper/2210.04695/) (2022)
- [Textual Entailment for Effective Triple Validation in Object Prediction](https://mlsanity.com/paper/2401.16293/) (2024)
- [Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning](https://mlsanity.com/paper/2211.07591/) (2022)
- [From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation](https://mlsanity.com/paper/2306.04170/) (2023)
- [EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion](https://mlsanity.com/paper/2402.09666/) (2024)
- [Explicit Inductive Inference using Large Language Models](https://mlsanity.com/paper/2408.14467/) (2024)
- [S2LPP: Small-to-Large Prompt Prediction across LLMs](https://mlsanity.com/paper/2505.20097/) (2025)

## Abstract (arXiv)

Typed entailment graphs try to learn the entailment relations between predicates from text and model them as edges between predicate nodes. The construction of entailment graphs usually suffers from severe sparsity and unreliability of distributional similarity. We propose a two-stage method, Entailment Graph with Textual Entailment and Transitivity (EGT2). EGT2 learns local entailment relations by recognizing possible textual entailment between template sentences formed by typed CCG-parsed predicates. Based on the generated local graph, EGT2 then uses three novel soft transitivity constraints to consider the logical transitivity in entailment structures. Experiments on benchmark datasets show that EGT2 can well model the transitivity in entailment graph to alleviate the sparsity issue, and lead to significant improvement over current state-of-the-art methods.