# When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions

arXiv: https://arxiv.org/abs/2108.13875 · HTML: https://mlsanity.com/paper/2108.13875/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [SG-Net: Syntax-Guided Machine Reading Comprehension](https://mlsanity.com/paper/1908.05147/) (2019)
- [Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network](https://mlsanity.com/paper/1905.03969/) (2019)
- [Dual Co-Matching Network for Multi-choice Reading Comprehension](https://mlsanity.com/paper/1901.09381/) (2019)
- [RikiNet: Reading Wikipedia Pages for Natural Question Answering](https://mlsanity.com/paper/2004.14560/) (2020)
- [TSQA: Tabular Scenario Based Question Answering](https://mlsanity.com/paper/2101.11429/) (2021)
- [MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension](https://mlsanity.com/paper/1910.00458/) (2019)
- [GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level](https://mlsanity.com/paper/1908.07855/) (2019)
- [Evidence Sentence Extraction for Machine Reading Comprehension](https://mlsanity.com/paper/1902.08852/) (2019)
- [Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering](https://mlsanity.com/paper/1901.00603/) (2019)
- [Answering questions by learning to rank -- Learning to rank by answering questions](https://mlsanity.com/paper/1909.00596/) (2019)
- [Enriching Documents with Compact, Representative, Relevant Knowledge Graphs](https://mlsanity.com/paper/2005.00153/) (2020)

## Built on by (most cited first, 3 of 3)

- [Clues Before Answers: Generation-Enhanced Multiple-Choice QA](https://mlsanity.com/paper/2205.00274/) (2022)
- [AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension](https://mlsanity.com/paper/2203.08992/) (2022)
- [GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks](https://mlsanity.com/paper/2608.07411/) (2026)

## Abstract (arXiv)

Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.