# Reading Comprehension as Natural Language Inference: A Semantic Analysis

arXiv: https://arxiv.org/abs/2010.01713 · HTML: https://mlsanity.com/paper/2010.01713/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification](https://mlsanity.com/paper/2402.03686/) (2024)
- [Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference](https://mlsanity.com/paper/2202.10408/) (2022)
- [ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection](https://mlsanity.com/paper/2209.13953/) (2022)
- [Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training](https://mlsanity.com/paper/2307.14666/) (2023)

## Abstract (arXiv)

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.