Papers with passage retrieval
Extractive NarrativeQA with Heuristic Pre-Training (D19-58)
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| Challenge: | Automated question answering (QA) from text remains a challenge for humans . a striking gap exists between machine and human performance on NLP tasks . |
| Approach: | They propose a heuristic extractive version of a data set to solve the problem of answer extraction rather than generation. |
| Outcome: | The proposed model outperforms previous models on summary-level QA from full narratives and on the METEOR metric. |
Open-Domain Conversational Question Answering with Historical Answers (2022.findings-aacl)
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| Challenge: | Existing approaches to conversational question answering are limited due to the large number of candidate documents. |
| Approach: | They propose a model that leverages historical answers to boost retrieval performance . they propose to use open-domain conversational question answering to solve these problems . |
| Outcome: | The proposed model outperforms baseline models in extractive and generative reader settings on OR-QuAC dataset. |
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval (2022.findings-emnlp)
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| Challenge: | Existing approaches to perform large-scale query-passage retrieval are term-based, but they lose interaction between query-pastage pairs. |
| Approach: | They propose to fuse query (passage) information into query representations via graph neural networks that are constructed by queries and their top retrieved passages. |
| Outcome: | The proposed model outperforms existing models on MSMARCO, Natural Questions and TriviaQA datasets and achieves the new state-of-the-art on these datasets. |
Open-Domain Question Answering Goes Conversational via Question Rewriting (2021.naacl-main)
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Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
| Challenge: | Existing large-scale benchmarks for conversational QA limit the topic of conversation to the content of a single document. |
| Approach: | They propose a dataset for Question Rewriting in Conversational Context (QReCC) the dataset contains 14K conversations with 80K question-answer pairs. |
| Outcome: | The proposed approach shows that the first baseline for the QReCC dataset is 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement. |
Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval (2023.findings-emnlp)
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| Challenge: | Existing approaches to enhance dense retrieval models are unwieldy, such as requiring explicit supervision, complex model architectures, or massive external models. |
| Approach: | They propose an unsupervised method to enhance passage retrieval in zero-shot settings by iterating a loop that a dense retriever learns from supervision signals provided by a reranker. |
| Outcome: | The proposed method outperforms leading supervised and unsupervised retrievers on the BEIR benchmark while showing strong adaptation abilities to tasks and domains that were unseen during training. |
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation (2025.findings-naacl)
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Yiruo Cheng, Kelong Mao, Ziliang Zhao, Guanting Dong, Hongjin Qian, Yongkang Wu, Tetsuya Sakai, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing research focuses on single-turn RAG, leaving a gap in addressing multi-turn conversations . a new benchmark is designed to assess RAG systems in realistic multi-turned conversations based on Wikipedia . |
| Approach: | They propose a large-scale benchmark to assess RAG systems in multi-turn contexts . CORAL includes diverse information-seeking conversations automatically derived from Wikipedia . authors propose unified framework to standardize various conversational RAG methods . |
| Outcome: | The proposed framework supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. |
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation (2021.eacl-main)
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| Challenge: | Recent advances in neural retrieval have led to advancements on document, passage and knowledge-base benchmarks. |
| Approach: | They propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. |
| Outcome: | The proposed approach can exceed term-based techniques on document retrieval benchmarks by using domain-targeted synthetic question generation. |
MatRank: Text Re-ranking by Latent Preference Matrix (2022.findings-emnlp)
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| Challenge: | Existing methods for text ranking have improved performance, but there are still challenges. |
| Approach: | They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix. |
| Outcome: | The proposed method outperforms all prior methods on datasets with extensive results. |
Robust Retrieval Augmented Generation for Zero-shot Slot Filling (2021.emnlp-main)
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| Challenge: | Automating high quality knowledge graphs from a given collection of documents remains a challenging problem in AI. |
| Approach: | They propose a novel approach to slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. |
| Outcome: | The proposed model improves on both T-REx and zsRE slot filling datasets and ranks at the top-1 position in the KILT leaderboard. |
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2021.emnlp-main)
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| Challenge: | Recent studies show that passage retrieval and passage reranking are important for achieving mutual improvement. |
| Approach: | They propose a unified listwise training approach for passage retrieval and passage reranking that incorporates a retrieval procedure and a hybrid data augmentation strategy. |
| Outcome: | The proposed approach improves on both MSMARCO and Natural Questions datasets. |
Improving Passage Retrieval with Zero-Shot Question Generation (2022.emnlp-main)
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Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer
| Challenge: | Existing re-ranking methods for open-domain question answering are not domain- or task-specific. |
| Approach: | They propose a simple and effective re-ranking method for improving passage retrieval in open-domain question answering. |
| Outcome: | The proposed method outperforms strong supervised models on open-domain questions and triviaQA datasets on top-1000 passages. |
DynRank: Improve Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification (2025.coling-main)
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Abdelrahman Elsayed Mahmoud Abdallah, Jamshid Mozafari, Bhawna Piryani, Mohammed M.Abdelgwad, Adam Jatowt
| Challenge: | Existing approaches to enhancing passage retrieval rely on static prompts and pre-defined templates. |
| Approach: | They propose a dynamic question classification framework for open-domain question-answering systems that generates contextually relevant prompts. |
| Outcome: | The proposed framework improves passage retrieval in open-domain questionanswering systems by generating contextually relevant prompts. |
CoRT: Complementary Rankings from Transformers (2021.naacl-main)
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| Challenge: | Recent approaches to information retrieval mitigate computational costs by using a multi-stage ranking pipeline. |
| Approach: | They propose a ranking model that leverages contextual representations from pre-trained language models to complement term-based ranking functions while causing no significant delay at query time. |
| Outcome: | The proposed model significantly increases candidate recall by complementing BM25 with missing candidates while causing no significant delay at query time. |
Making Information Seeking Easier: An Improved Pipeline for Conversational Search (2020.findings-emnlp)
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| Challenge: | Existing tools for conversational information seeking (CIS) do not support conversational contexts. |
| Approach: | They propose a highly effective pipeline for passage retrieval in a conversational search setting using a BERT-based classifier and a multi-view reranking component. |
| Outcome: | The proposed pipeline achieves 14.8% performance improvement over the current state-of-the-art pipeline and surpasses the Oracle. |
Optimizing Test-Time Query Representations for Dense Retrieval (2023.findings-acl)
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| Challenge: | Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. |
| Approach: | They propose a test-time optimization of query representations that provides fine-grained pseudo labels over retrieval results. |
| Outcome: | The proposed algorithm improves open-domain question answering accuracy and direct re-ranking by up to 2.0% while running 1.3–2.4x faster with an efficient implementation. |
DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (2022.emnlp-main)
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| Challenge: | Existing datasets for non-English passage retrieval are lacking in quality and accuracy. |
| Approach: | They present a large-scale Chinese dataset for passage retrieval . they reduce false negatives by manually annotating results pooled from multiple retrievers . |
| Outcome: | The proposed dataset reduces false negatives in development and testing sets and removes similar training queries. |
Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval (2026.findings-acl)
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Junyoung Kim, Anton Korikov, Jiazhou Liang, Justin Cui, Yifan Simon Liu, Qianfeng Wen, Mark Zhao, Scott Sanner
| Challenge: | Existing approaches passively rely on first-stage dense retrievers, which leads to two limitations: failing to retrieve relevant passages in semantically distinct clusters and failing to propagate relevance signals to the broader corpus. |
| Approach: | They propose a framework that propagates sparse LLM relevance signals across the embedding space to guide global exploration. |
| Outcome: | Experiments show that the proposed framework outperforms existing approaches under the same budget on all four datasets. |
Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval (2021.emnlp-main)
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| Challenge: | Existing neural firststage retrieval models overcome lexical gap issue by projecting query and document to a shared dense space. |
| Approach: | They propose a multi-stage framework for neural passage retrieval using synthetic data, negative sampling, and fusion techniques. |
| Outcome: | The proposed framework improves retrieval accuracy and enhances the negative contrast in both stages. |
SCAI-QReCC Shared Task on Conversational Question Answering (2022.lrec-1)
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| Challenge: | evaluating systems for conversational QA remains an open research problem in its own right . evaluating (conversational) QA systems remains an important challenge for developing conversational information retrieval (conversional search) systems. |
| Approach: | They propose to use a conversational question answering task to extend the original conversational QA dataset with alternative correct answers produced by participant systems. |
| Outcome: | The proposed task was based on the SCAI-QReCC 2021 shared task on conversational question answering. |
A Deep Metric Learning Method for Biomedical Passage Retrieval (2020.coling-main)
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| Challenge: | Existing methods for passage retrieval are based on metric learning . the proposed approach is particularly well suited for domain-specific passage retrievals where it is very important to take into account different sources of information. |
| Approach: | They propose a method that learns a metric for questions and passages based on their internal semantic interactions. |
| Outcome: | The proposed method outperforms triplet loss and state-of-the-art methods in a biomedical passage retrieval task and outperformed triplet losses by 10% and 26%. |
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
| Challenge: | Open-domain question answering relies on efficient passage retrieval to select candidate contexts. |
| Approach: | They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages. |
| Outcome: | The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks. |
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval (2021.emnlp-main)
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| Challenge: | Using self-training to train unsupervised domains can be expensive, resulting in poor generalization due to distributional shift. |
| Approach: | They propose to use unaligned data to train unsupervised domain adaptation models using cheap synthetically generated labeled data. |
| Outcome: | The proposed method significantly outperforms self-training on question generation and passage retrieval domains and on MLQuestions and PubMedQA. |
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA (2024.findings-emnlp)
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| Challenge: | Understanding users’ contextual search intent when generating responses is an understudied topic for conversational question answering (QA). |
| Approach: | They propose a method that allows LLMs to decide when to retrieve in RAG settings given a conversational context. |
| Outcome: | The proposed method improves on three conversational QA datasets and criticizes the quality of generated responses. |
Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search (2026.findings-acl)
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| Challenge: | Existing approaches to rewrite ambiguous queries ignore feedback from query rewriting, passage retrieval and response generation in the rewritten process. |
| Approach: | They propose to construct self-consistent preference alignment data to generate more diverse rewritten queries. |
| Outcome: | The proposed method is effective in both in- and out-of-distribution scenarios. |
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2022.emnlp-main)
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Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar
| Challenge: | Existing models for conversational question answering require specific retrievers to understand user questions. |
| Approach: | They develop a query rewriting model CONQRR that rewrites a conversational question into a standalone question. |
| Outcome: | The proposed model achieves state-of-the-art on an open-domain conversational question answering dataset and is effective for two different off-the shelf retrievers. |
Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers (2024.emnlp-main)
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| Challenge: | Existing methods to incorporate retriever’s preference during the training of query rewriting models rely on extensive annotations such as in-domain rewrites and/or relevant passage labels, limiting their generalization and adaptation capabilities. |
| Approach: | They propose a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label. |
| Outcome: | The proposed approach decontexualizes conversational queries into self-contained questions suitable for off-the-shelf retrievers. |
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points . dense retrievers are limited by their inability to perform semantic matching for relevant passages that have low lexical overlap with the query. |
| Approach: | They propose a query expansion and reranking approach for improving passage retrieval with the application to open-domain question answering. |
| Outcome: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points when compared to a vanilla query expansion model and a dense retrieval model. |
Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting (2023.findings-emnlp)
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| Challenge: | Existing approaches to identifying ambiguous questions as part of a conversation have not addressed this challenge. |
| Approach: | They propose a multi-task learning approach that uses a text generation model for question rewriting and classification. |
| Outcome: | The proposed approach outperforms single-task learning baselines on three LIF test sets. |
PolQA: Polish Question Answering Dataset (2024.lrec-main)
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| Challenge: | Recent proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. |
| Approach: | They propose an efficient annotation strategy that increases passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82%. |
| Outcome: | The proposed approach increases passage retrieval accuracy @10 by 10.55 p.p. while reducing the annotation cost by 82%. |