# Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval

arXiv: https://arxiv.org/abs/2311.02616 · HTML: https://mlsanity.com/paper/2311.02616/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 47 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Builds on (most cited first, 29 of 29)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering](https://mlsanity.com/paper/1911.10470/) (2019)
- [A Deep Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/1711.08611/) (2017)
- [Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://mlsanity.com/paper/1906.02916/) (2019)
- [Unsupervised Question Decomposition for Question Answering](https://mlsanity.com/paper/2002.09758/) (2020)
- [End-to-End Training of Neural Retrievers for Open-Domain Question Answering](https://mlsanity.com/paper/2101.00408/) (2021)
- [Learning to Match Using Local and Distributed Representations of Text for Web Search](https://mlsanity.com/paper/1610.08136/) (2016)
- [Answering Complex Open-domain Questions Through Iterative Query Generation](https://mlsanity.com/paper/1910.07000/) (2019)
- [Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations](https://mlsanity.com/paper/2102.10073/) (2021)
- [Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers](https://mlsanity.com/paper/2107.05687/) (2021)
- [Optimizing Rank-based Metrics with Blackbox Differentiation](https://mlsanity.com/paper/1912.03500/) (2019)
- [Revealing the Importance of Semantic Retrieval for Machine Reading at Scale](https://mlsanity.com/paper/1909.08041/) (2019)
- [Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering](https://mlsanity.com/paper/1905.05733/) (2019)
- [Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable](https://mlsanity.com/paper/2110.13472/) (2021)
- [Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning](https://mlsanity.com/paper/1909.05803/) (2019)
- [Complementing Lexical Retrieval with Semantic Residual Embedding](https://mlsanity.com/paper/2004.13969/) (2020)
- [Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space](https://mlsanity.com/paper/2004.08046/) (2020)
- [From FiLM to Video: Multi-turn Question Answering with Multi-modal Context](https://mlsanity.com/paper/1812.07023/) (2018)
- [Introducing MathQA -- A Math-Aware Question Answering System](https://mlsanity.com/paper/1907.01642/) (2019)
- [A Study of MatchPyramid Models on Ad-hoc Retrieval](https://mlsanity.com/paper/1606.04648/) (2016)
- [Contextualized Word Representations for Reading Comprehension](https://mlsanity.com/paper/1712.03609/) (2017)

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

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering](https://mlsanity.com/paper/2406.07348/) (2024)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

Lexical and semantic matches are commonly used as relevance measurements for information retrieval. Together they estimate the semantic equivalence between the query and the candidates. However, semantic equivalence is not the only relevance signal that needs to be considered when retrieving evidences for multi-hop questions. In this work, we demonstrate that textual entailment relation is another important relevance dimension that should be considered. To retrieve evidences that are either semantically equivalent to or entailed by the question simultaneously, we divide the task of evidence retrieval for multi-hop question answering (QA) into two sub-tasks, i.e., semantic textual similarity and inference similarity retrieval. We propose two ensemble models, EAR and EARnest, which tackle each of the sub-tasks separately and then jointly re-rank sentences with the consideration of the diverse relevance signals. Experimental results on HotpotQA verify that our models not only significantly outperform all the single retrieval models it is based on, but is also more effective than two intuitive ensemble baseline models.