# A Survey on Explainability in Machine Reading Comprehension

arXiv: https://arxiv.org/abs/2010.00389 · HTML: https://mlsanity.com/paper/2010.00389/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 30 of 33)

- [Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing](https://mlsanity.com/paper/2102.12060/) (2021)
- [Rationalization for Explainable NLP: A Survey](https://mlsanity.com/paper/2301.08912/) (2023)
- [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://mlsanity.com/paper/2105.12936/) (2021)
- [Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration](https://mlsanity.com/paper/2107.11879/) (2021)
- [METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation](https://mlsanity.com/paper/2205.02593/) (2022)
- [Faithful Question Answering with Monte-Carlo Planning](https://mlsanity.com/paper/2305.02556/) (2023)
- [Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement](https://mlsanity.com/paper/2402.00745/) (2024)
- [Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making](https://mlsanity.com/paper/2204.05030/) (2022)
- [Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards](https://mlsanity.com/paper/2105.01974/) (2021)
- [Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension](https://mlsanity.com/paper/2109.06853/) (2021)
- [Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.00731/) (2022)
- [Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference](https://mlsanity.com/paper/2105.03417/) (2021)
- [Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders](https://mlsanity.com/paper/2402.00723/) (2024)
- [On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings](https://mlsanity.com/paper/2109.03334/) (2021)
- [A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data](https://mlsanity.com/paper/2207.00812/) (2022)
- [HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision](https://mlsanity.com/paper/2305.14237/) (2023)
- [Inference to the Best Explanation in Large Language Models](https://mlsanity.com/paper/2402.10767/) (2024)
- [Encoding Explanatory Knowledge for Zero-shot Science Question Answering](https://mlsanity.com/paper/2105.05737/) (2021)
- [Generating Hypothetical Events for Abductive Inference](https://mlsanity.com/paper/2106.03973/) (2021)
- [Do Transformers Encode a Foundational Ontology? Probing Abstract Classes in Natural Language](https://mlsanity.com/paper/2201.10262/) (2022)
- [SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning](https://mlsanity.com/paper/2401.13246/) (2024)
- [SuMe: A Dataset Towards Summarizing Biomedical Mechanisms](https://mlsanity.com/paper/2205.04652/) (2022)
- [Machine Reading Comprehension using Case-based Reasoning](https://mlsanity.com/paper/2305.14815/) (2023)
- [Scientific Explanation and Natural Language: A Unified Epistemological-Linguistic Perspective for Explainable AI](https://mlsanity.com/paper/2205.01809/) (2022)
- [Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers](https://mlsanity.com/paper/2208.03339/) (2022)
- [A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics](https://mlsanity.com/paper/2212.04070/) (2022)
- [A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches](https://mlsanity.com/paper/2212.04072/) (2022)
- [Towards Controllable Natural Language Inference through Lexical Inference Types](https://mlsanity.com/paper/2308.03581/) (2023)
- [A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference](https://mlsanity.com/paper/2404.02625/) (2024)
- [Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives](https://mlsanity.com/paper/2208.01376/) (2022)

## Abstract (arXiv)

This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle these challenges. We also present the evaluation methodologies to assess the performance of explainable systems. In addition, we identify persisting open research questions and highlight critical directions for future work.