# Evidence Sentence Extraction for Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1902.08852 · HTML: https://mlsanity.com/paper/1902.08852/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2019.

## Built on by (most cited first, 14 of 14)

- [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://mlsanity.com/paper/2105.12936/) (2021)
- [Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation](https://mlsanity.com/paper/2201.05088/) (2022)
- [You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos](https://mlsanity.com/paper/2205.12886/) (2022)
- [What's in your Head? Emergent Behaviour in Multi-Task Transformer Models](https://mlsanity.com/paper/2104.06129/) (2021)
- [Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports](https://mlsanity.com/paper/2304.13180/) (2023)
- [Post-Hoc Answer Attribution for Grounded and Trustworthy Long Document Comprehension: Task, Insights, and Challenges](https://mlsanity.com/paper/2406.06938/) (2024)
- [ExpMRC: Explainability Evaluation for Machine Reading Comprehension](https://mlsanity.com/paper/2105.04126/) (2021)
- [When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions](https://mlsanity.com/paper/2108.13875/) (2021)
- [Classification of Goods Using Text Descriptions With Sentences Retrieval](https://mlsanity.com/paper/2111.01663/) (2021)
- [Explainable Product Classification for Customs](https://mlsanity.com/paper/2311.10922/) (2023)
- [Multi-grained Evidence Inference for Multi-choice Reading Comprehension](https://mlsanity.com/paper/2310.18070/) (2023)
- [OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context](https://mlsanity.com/paper/2407.15736/) (2024)
- [U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension](https://mlsanity.com/paper/2210.02621/) (2022)
- [EMBRACE: Evaluation and Modifications for Boosting RACE](https://mlsanity.com/paper/2305.08433/) (2023)

## Abstract (arXiv)

Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor. To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision. We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiple-choice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document. To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC.