# Do Answers to Boolean Questions Need Explanations? Yes

arXiv: https://arxiv.org/abs/2112.07772 · HTML: https://mlsanity.com/paper/2112.07772/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://mlsanity.com/paper/2101.02235/) (2021)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [The Fact Extraction and VERification (FEVER) Shared Task](https://mlsanity.com/paper/1811.10971/) (2018)
- [Tailor: Generating and Perturbing Text with Semantic Controls](https://mlsanity.com/paper/2107.07150/) (2021)
- [Poolingformer: Long Document Modeling with Pooling Attention](https://mlsanity.com/paper/2105.04371/) (2021)
- [A BERT Baseline for the Natural Questions](https://mlsanity.com/paper/1901.08634/) (2019)
- [ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion](https://mlsanity.com/paper/2006.12146/) (2020)
- [A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction](https://mlsanity.com/paper/2005.05189/) (2020)
- [DaNetQA: a yes/no Question Answering Dataset for the Russian Language](https://mlsanity.com/paper/2010.02605/) (2020)
- [No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension](https://mlsanity.com/paper/2009.12056/) (2020)
- [Structural analysis of an all-purpose question answering model](https://mlsanity.com/paper/2104.06045/) (2021)

## Built on by (most cited first, 5 of 5)

- [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)
- [GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions](https://mlsanity.com/paper/2206.08441/) (2022)
- [Controllable Decontextualization of Yes/No Question and Answers into Factual Statements](https://mlsanity.com/paper/2401.09775/) (2024)
- [Diverse LLMs or Diverse Question Interpretations? That is the Ensembling Question](https://mlsanity.com/paper/2507.21168/) (2025)

## Abstract (arXiv)

Existing datasets that contain boolean questions, such as BoolQ and TYDI QA , provide the user with a YES/NO response to the question. However, a one word response is not sufficient for an explainable system. We promote explainability by releasing a new set of annotations marking the evidence in existing TyDi QA and BoolQ datasets. We show that our annotations can be used to train a model that extracts improved evidence spans compared to models that rely on existing resources. We confirm our findings with a user study which shows that our extracted evidence spans enhance the user experience. We also provide further insight into the challenges of answering boolean questions, such as passages containing conflicting YES and NO answers, and varying degrees of relevance of the predicted evidence.