# Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering

arXiv: https://arxiv.org/abs/1810.00494 · HTML: https://mlsanity.com/paper/1810.00494/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 24 of 24)

- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering](https://mlsanity.com/paper/2109.04014/) (2021)
- [R2-D2: A Modular Baseline for Open-Domain Question Answering](https://mlsanity.com/paper/2109.03502/) (2021)
- [Adaptive Information Seeking for Open-Domain Question Answering](https://mlsanity.com/paper/2109.06747/) (2021)
- [Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering](https://mlsanity.com/paper/2101.00294/) (2021)
- [Few-shot Reranking for Multi-hop QA via Language Model Prompting](https://mlsanity.com/paper/2205.12650/) (2022)
- [Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?](https://mlsanity.com/paper/2211.12707/) (2022)
- [Pruning the Index Contents for Memory Efficient Open-Domain QA](https://mlsanity.com/paper/2102.10697/) (2021)
- [Multimodal Reranking for Knowledge-Intensive Visual Question Answering](https://mlsanity.com/paper/2407.12277/) (2024)
- [ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking](https://mlsanity.com/paper/2509.00520/) (2025)
- [Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer](https://mlsanity.com/paper/2311.06720/) (2023)
- [Weakly-Supervised Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2103.02537/) (2021)
- [Performance Prediction for Multi-hop Questions](https://mlsanity.com/paper/2308.06431/) (2023)
- [EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System](https://mlsanity.com/paper/2101.02157/) (2021)
- [Model Agnostic Answer Reranking System for Adversarial Question Answering](https://mlsanity.com/paper/2102.03016/) (2021)
- [A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections](https://mlsanity.com/paper/2106.08908/) (2021)
- [Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking](https://mlsanity.com/paper/2107.05002/) (2021)
- [SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering](https://mlsanity.com/paper/2101.01910/) (2021)
- [Recent Advances in Automated Question Answering In Biomedical Domain](https://mlsanity.com/paper/2111.05937/) (2021)
- [Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2301.00429/) (2023)
- [Rank Your Summaries: Enhancing Bengali Text Summarization via Ranking-based Approach](https://mlsanity.com/paper/2307.07392/) (2023)
- [Perturbation-based Active Learning for Question Answering](https://mlsanity.com/paper/2311.02345/) (2023)
- [VerAs: Verify then Assess STEM Lab Reports](https://mlsanity.com/paper/2402.05224/) (2024)
- [RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents](https://mlsanity.com/paper/2502.12124/) (2025)

## Abstract (arXiv)

Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four open-domain QA datasets by 7.8% on average.