# Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension

arXiv: https://arxiv.org/abs/1808.10628 · HTML: https://mlsanity.com/paper/1808.10628/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Knowledge Graph Prompting for Multi-Document Question Answering](https://mlsanity.com/paper/2308.11730/) (2023)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval](https://mlsanity.com/paper/2203.03367/) (2022)
- [Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework](https://mlsanity.com/paper/2109.05897/) (2021)
- [FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipeline](https://mlsanity.com/paper/2410.13959/) (2024)
- [Continual Learning of Long Topic Sequences in Neural Information Retrieval](https://mlsanity.com/paper/2201.03356/) (2022)
- [Investigating Information Inconsistency in Multilingual Open-Domain Question Answering](https://mlsanity.com/paper/2205.12456/) (2022)
- [ISA-bEL: Intelligent Search Algorithm based on Entity Linking](https://mlsanity.com/paper/2205.04322/) (2022)

## Abstract (arXiv)

This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages in a knowledge source and then carries out the reading comprehension (RC) task of extracting an answer span from the passages. Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages. In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans. Experimental results on the standard benchmark, answering SQuAD questions using the full Wikipedia as the knowledge source, showed that our model achieved state-of-the-art performance. Moreover, we thoroughly evaluated the individual contributions of our model components with our new Japanese dataset and SQuAD. The results showed significant improvements in the IR task and provided a new perspective on IR for RC: it is effective to teach which part of the passage answers the question rather than to give only a relevance score to the whole passage.