# Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards

arXiv: https://arxiv.org/abs/2105.01974 · HTML: https://mlsanity.com/paper/2105.01974/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 24 of 24)

- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [QASC: A Dataset for Question Answering via Sentence Composition](https://mlsanity.com/paper/1910.11473/) (2019)
- [What Will it Take to Fix Benchmarking in Natural Language Understanding?](https://mlsanity.com/paper/2104.02145/) (2021)
- [Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets](https://mlsanity.com/paper/2008.02637/) (2020)
- [NILE : Natural Language Inference with Faithful Natural Language Explanations](https://mlsanity.com/paper/2005.12116/) (2020)
- [Compositional Questions Do Not Necessitate Multi-hop Reasoning](https://mlsanity.com/paper/1906.02900/) (2019)
- [Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations](https://mlsanity.com/paper/1910.03065/) (2019)
- [WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference](https://mlsanity.com/paper/1802.03052/) (2018)
- [Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering](https://mlsanity.com/paper/2010.03274/) (2020)
- [SemEval-2020 Task 4: Commonsense Validation and Explanation](https://mlsanity.com/paper/2007.00236/) (2020)
- [A Survey on Explainability in Machine Reading Comprehension](https://mlsanity.com/paper/2010.00389/) (2020)
- [Obtaining Faithful Interpretations from Compositional Neural Networks](https://mlsanity.com/paper/2005.00724/) (2020)
- [A Framework for Evaluation of Machine Reading Comprehension Gold Standards](https://mlsanity.com/paper/2003.04642/) (2020)
- [Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text](https://mlsanity.com/paper/2004.14959/) (2020)
- [Multi-class Hierarchical Question Classification for Multiple Choice Science Exams](https://mlsanity.com/paper/1908.05441/) (2019)
- [Autoregressive Reasoning over Chains of Facts with Transformers](https://mlsanity.com/paper/2012.11321/) (2020)
- [ExplanationLP: Abductive Reasoning for Explainable Science Question Answering](https://mlsanity.com/paper/2010.13128/) (2020)

## Built on by (most cited first, 12 of 12)

- [Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks](https://mlsanity.com/paper/2207.13243/) (2022)
- [Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving](https://mlsanity.com/paper/2405.01379/) (2024)
- [Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement](https://mlsanity.com/paper/2402.00745/) (2024)
- [Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic](https://mlsanity.com/paper/2402.14798/) (2024)
- [Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI](https://mlsanity.com/paper/2205.12469/) (2022)
- [Inference to the Best Explanation in Large Language Models](https://mlsanity.com/paper/2402.10767/) (2024)
- [Scientific Explanation and Natural Language: A Unified Epistemological-Linguistic Perspective for Explainable AI](https://mlsanity.com/paper/2205.01809/) (2022)
- [Towards Controllable Natural Language Inference through Lexical Inference Types](https://mlsanity.com/paper/2308.03581/) (2023)
- [Multi-Perspective LLM Annotations for Valid Analyses in Subjective Tasks](https://mlsanity.com/paper/2603.21404/) (2026)
- [Decompose-and-Formalise: Recursively Verifiable Natural Language Inference](https://mlsanity.com/paper/2601.19605/) (2026)
- [Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization](https://mlsanity.com/paper/2606.09449/) (2026)
- [Reference-Free Evaluation of Reasoning in Open-Ended Question Answering](https://mlsanity.com/paper/2607.19678/) (2026)

## Abstract (arXiv)

An emerging line of research in Explainable NLP is the creation of datasets enriched with human-annotated explanations and rationales, used to build and evaluate models with step-wise inference and explanation generation capabilities. While human-annotated explanations are used as ground-truth for the inference, there is a lack of systematic assessment of their consistency and rigour. In an attempt to provide a critical quality assessment of Explanation Gold Standards (XGSs) for NLI, we propose a systematic annotation methodology, named Explanation Entailment Verification (EEV), to quantify the logical validity of human-annotated explanations. The application of EEV on three mainstream datasets reveals the surprising conclusion that a majority of the explanations, while appearing coherent on the surface, represent logically invalid arguments, ranging from being incomplete to containing clearly identifiable logical errors. This conclusion confirms that the inferential properties of explanations are still poorly formalised and understood, and that additional work on this line of research is necessary to improve the way Explanation Gold Standards are constructed.