# Multi-Hop Paragraph Retrieval for Open-Domain Question Answering

arXiv: https://arxiv.org/abs/1906.06606 · HTML: https://mlsanity.com/paper/1906.06606/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2019.

## Built on by (most cited first, 30 of 34)

- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval](https://mlsanity.com/paper/2101.00436/) (2021)
- [FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge](https://mlsanity.com/paper/2305.08281/) (2023)
- [Adaptive Information Seeking for Open-Domain Question Answering](https://mlsanity.com/paper/2109.06747/) (2021)
- [Visconde: Multi-document QA with GPT-3 and Neural Reranking](https://mlsanity.com/paper/2212.09656/) (2022)
- [Reasoning over Public and Private Data in Retrieval-Based Systems](https://mlsanity.com/paper/2203.11027/) (2022)
- [Chain-of-Skills: A Configurable Model for Open-domain Question Answering](https://mlsanity.com/paper/2305.03130/) (2023)
- [Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering](https://mlsanity.com/paper/2105.11776/) (2021)
- [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions](https://mlsanity.com/paper/2109.12655/) (2021)
- [MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning](https://mlsanity.com/paper/2510.13614/) (2025)
- [Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge](https://mlsanity.com/paper/2210.12338/) (2022)
- [Enhancing textual textbook question answering with large language models and retrieval augmented generation](https://mlsanity.com/paper/2402.05128/) (2024)
- [PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation](https://mlsanity.com/paper/2502.20377/) (2025)
- [Multi-hop Evidence Retrieval for Cross-document Relation Extraction](https://mlsanity.com/paper/2212.10786/) (2022)
- [From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering](https://mlsanity.com/paper/2205.11729/) (2022)
- [Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language](https://mlsanity.com/paper/2205.06111/) (2022)
- [OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries](https://mlsanity.com/paper/2605.06235/) (2026)
- [A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering](https://mlsanity.com/paper/2104.08443/) (2021)
- [Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks](https://mlsanity.com/paper/2305.08495/) (2023)
- [Unbalanced Optimal Transport for Unbalanced Word Alignment](https://mlsanity.com/paper/2306.04116/) (2023)
- [Submodular Framework for Structured-Sparse Optimal Transport](https://mlsanity.com/paper/2406.04914/) (2024)
- [ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains](https://mlsanity.com/paper/2210.08763/) (2022)
- [Rethinking Label Smoothing on Multi-hop Question Answering](https://mlsanity.com/paper/2212.09512/) (2022)
- [Performance Prediction for Multi-hop Questions](https://mlsanity.com/paper/2308.06431/) (2023)
- [Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering](https://mlsanity.com/paper/2403.12393/) (2024)
- [Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering](https://mlsanity.com/paper/2302.05963/) (2023)
- [Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment](https://mlsanity.com/paper/2306.04841/) (2023)
- [Question Answering with Deep Neural Networks for Semi-Structured Heterogeneous Genealogical Knowledge Graphs](https://mlsanity.com/paper/2307.16214/) (2023)

## Abstract (arXiv)

This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, respectively.