mlsanityData to 7 Oct 2026

Entailment Graph Learning with Textual Entailment and Soft Transitivity

arXiv 2204.03286 ↗First posted 7 Apr 2022cs.CLcited by 11

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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.

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

Builds on

11 of 38 references.

References · 11

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  3. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  4. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  5. 2019A Logic-Driven Framework for Consistency of Neural Modelscited by 64
  6. 2019Probabilistic Models of Relational Implicationcited by 17
  7. 2021Multivalent Entailment Graphs for Question Answeringcited by 10
  8. 2021Language Models for Lexical Inference in Contextcited by 9
  9. 2021Incorporating Temporal Information in Entailment Graph Miningcited by 7
  10. 2018Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inferencecited by 5
  11. 2021Continuous Entailment Patterns for Lexical Inference in Contextcited by 3

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Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 4; 2025: 1.

11 papers.

  1. 2023AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graphcited by 17
  2. 2024AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimationcited by 14
  3. 2022Smoothing Entailment Graphs with Language Modelscited by 8
  4. 2023From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarizationcited by 8
  5. 2022Language Models Are Poor Learners of Directional Inferencecited by 4
  6. 2024Textual Entailment for Effective Triple Validation in Object Predictioncited by 2
  7. 2022Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planningcited by 1
  8. 2023From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generationcited by 0
  9. 2024EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completioncited by 0
  10. 2024Explicit Inductive Inference using Large Language Modelscited by 0
  11. 2025S2LPP: Small-to-Large Prompt Prediction across LLMscited by 0