# Multivalent Entailment Graphs for Question Answering

arXiv: https://arxiv.org/abs/2104.07846 · HTML: https://mlsanity.com/paper/2104.07846/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Probabilistic Models of Relational Implication](https://mlsanity.com/paper/1907.12048/) (2019)
- [Distributional Inclusion Hypothesis for Tensor-based Composition](https://mlsanity.com/paper/1610.04416/) (2016)

## Built on by (most cited first, 10 of 10)

- [Sources of Hallucination by Large Language Models on Inference Tasks](https://mlsanity.com/paper/2305.14552/) (2023)
- [Editing Conceptual Knowledge for Large Language Models](https://mlsanity.com/paper/2403.06259/) (2024)
- [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)
- [Language Models Are Poor Learners of Directional Inference](https://mlsanity.com/paper/2210.04695/) (2022)
- [Continuous Entailment Patterns for Lexical Inference in Context](https://mlsanity.com/paper/2109.03695/) (2021)
- [Blindness to Modality Helps Entailment Graph Mining](https://mlsanity.com/paper/2109.10227/) (2021)
- [From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation](https://mlsanity.com/paper/2306.04170/) (2023)

## Abstract (arXiv)

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding. There has been much progress in unsupervised learning of entailment graphs for this purpose. We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) entails WIN(Biden); (2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task. We show that directional entailment is more helpful for inference than bidirectional similarity on questions of fine-grained semantics. We also show that drawing on evidence across valencies answers more questions than by using only the same valency evidence.