Papers with document retrieval
Applying BERT to Document Retrieval with Birch (D19-3)
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| Challenge: | Birch is an open-source document retrieval system that integrates with the Anserini information retrieval toolkit to demonstrate end-to-end search over large document collections. |
| Approach: | They propose to integrate Anserini with a BERT-based document ranking model that provides an end-to-end open-source search engine. |
| Outcome: | The proposed system outperforms existing approaches to document retrieval and question answering on standard newswire and social media test collections. |
Integrating Stance Detection and Fact Checking in a Unified Corpus (N18-2)
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| Challenge: | Existing methods for fact checking are not supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. |
| Approach: | They propose to implement automatic fact checking on an Arabic fact checking corpus, which is the first of its kind. |
| Outcome: | The proposed approach is based on an Arabic fact checking corpus, the first of its kind. |
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)
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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han
| Challenge: | EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences. |
| Approach: | They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
| Outcome: | EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
FAKTA: An Automatic End-to-End Fact Checking System (N19-4)
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| Challenge: | Existing studies have investigated individual components of fact checking process but none offer such a capability. |
| Approach: | They propose a framework that integrates various components of a fact-checking process. |
| Outcome: | The proposed framework integrates various components of a fact-checking process to predict the factuality of claims and provide evidence at the document and sentence level to explain its predictions. |
Design Challenges for a Multi-Perspective Search Engine (2022.findings-naacl)
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| Challenge: | a document retrieval system fails to deliver diverse and direct responses to controversial questions . classical document retrievals provide a ranked list of references to relevant but not necessarily trustworthy web documents . |
| Approach: | They propose a perspective-oriented document retrieval paradigm to address these challenges . they propose sponses with different perspectives within topically-related web documents . |
| Outcome: | The proposed system is based on a user survey and a prototype . it will be used to assess the utility and understanding of the system . |
Technical Question Answering across Tasks and Domains (2021.naacl-industry)
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| Challenge: | Existing methods for technical QA have a limited data size and question and answer overlaps . |
| Approach: | They propose a framework of deep transfer learning to address technical QA across tasks and domains using document retrieval and reading comprehension tasks. |
| Outcome: | The proposed framework performs better than state-of-the-art methods on the TechQA task. |
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)
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| Challenge: | Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents . |
| Approach: | They investigate how entity coreference affects document retrieval and generative performance in RAG-based systems. |
| Outcome: | The proposed model improves QA performance and retrieval relevance and contextual understanding. |
Automating Horizon Scanning in Future Studies (2022.lrec-1)
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Tatsuya Ishigaki, Suzuko Nishino, Sohei Washino, Hiroki Igarashi, Yukari Nagai, Yuichi Washida, Akihiko Murai
| Challenge: | Existing studies collect enough information to predict drastic social changes in the mid- or long-term future. |
| Approach: | They propose document retrieval and comment generation tasks for automating horizon scanning by analyzing a dataset that contains 2,266 manually collected news articles with comments written by experts. |
| Outcome: | The proposed tasks are more efficient than previous methods and the proposed models are more accurate. |
Q2R: A Query-to-Resolution System for Natural-Language Queries (2022.naacl-industry)
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| Challenge: | Existing text ranking methods are expensive since they require a parametric classifier to retrieve a small D D. |
| Approach: | They propose a system that combines direct classification with standard content-based retrieval approaches to significantly improve the relevance of retrieved documents. |
| Outcome: | The proposed system improves the relevance of retrieved documents by using a novel Q2R orchestration framework. |
LongEmbed: Extending Embedding Models for Long Context Retrieval (2024.emnlp-main)
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| Challenge: | Existing embedding models support only 512 input tokens, hindering their application in scenarios requiring long inputs. |
| Approach: | They evaluate the performance of existing embedding models by using a new benchmark and a training-free context window extension strategy. |
| Outcome: | The proposed model extends the input window of existing models by several folds. |
RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)
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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries. |
| Approach: | They propose a framework for training query rewriting models that leverages a reranker framework. |
| Outcome: | The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models. |
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization (2022.emnlp-main)
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| Challenge: | Existing semantic hashing methods only learn a binary code for each document and use Hamming distance to evaluate document distances. |
| Approach: | They propose to leverage BERT embeddings to perform efficient retrieval based on product quantization technique . they transform original BERT embedded codewords and feed it into a probabilistic product quantizer module . |
| Outcome: | The proposed method outperforms current state-of-the-art methods on three benchmarks. |
Adaptive Document Retrieval for Deep Question Answering (D18-1)
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| Challenge: | Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension. |
| Approach: | They propose an adaptive document retrieval model that learns the optimal document number, conditional on the size of the corpus and the query. |
| Outcome: | The proposed model outperforms state-of-the-art methods on multiple benchmark datasets and in the context of corpora with variable sizes. |
Weakly Supervised Pre-Training for Multi-Hop Retriever (2021.findings-acl)
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| Challenge: | Existing methods for weakly supervised multi-hop pretraining require costly human annotation. |
| Approach: | They propose a method for weakly supervised multi-hop retriever pretraining without human efforts by generating vector representations of complex questions and subquestion as weak supervision for pre-training. |
| Outcome: | The proposed method is effective and robust on limited data and computational resources. |
A Dynamic Self-Evolving Extraction System (2026.acl-demo)
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| Challenge: | High-quality information extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers. |
| Approach: | They propose a Dynamic Self-Evolving Extraction and Curation Toolkit which continuously improves as it is used to extract structured information from raw text. |
| Outcome: | The proposed toolkit continuously improves as it is used in medical, legal, and HR domains. |
In-Context Retrieval-Augmented Language Models (2023.tacl-1)
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| Challenge: | Existing RALM methods focus on modifying the LM architecture to facilitate incorporation of external information, complicating deployment. |
| Approach: | They propose to condition a language model on relevant documents from a grounding corpus during generation by conditioning on external knowledge sources. |
| Outcome: | The proposed method significantly improves language modeling performance and provides natural source attribution mechanism. |
REVISE: A Framework for Revising OCRed text in Practical Information Systems with Data Contamination Strategy (2025.acl-industry)
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| Challenge: | Existing Document AI frameworks lack the capability to structurally organize and manage document information. |
| Approach: | They propose a framework that corrects OCR errors at the character, word, and structural levels and a synthetic data generation strategy that realistically simulates such errors to train an effective correction model. |
| Outcome: | The proposed framework improves document retrieval and question answering tasks by correcting errors introduced by OCR errors at the character, word, and structural levels. |
Keyphrase Generation for Scientific Document Retrieval (2020.acl-main)
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| Challenge: | Sequence-to-sequence models have been used to generate keyphrases, but it is unclear whether they are reliable enough for document retrieval. |
| Approach: | They propose a framework for extrinsic evaluation that allows for a better understanding of the limitations of keyphrase generation models. |
| Outcome: | The proposed models improve retrieval performance by supplementing documents with keyphrases that are not present in the source text and generalizing models across domains. |
Is Semantic Chunking Worth the Computational Cost? (2025.findings-naacl)
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| Challenge: | Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking. |
| Approach: | They evaluate the effectiveness of semantic chunking using three common retrieval tasks . they find that the computational costs associated with semantic chunks are not justified by consistent performance gains. |
| Outcome: | The proposed semantic chunking approach is not able to deliver consistent performance gains in three retrieval-related tasks. |
How Robust are Fact Checking Systems on Colloquial Claims? (2021.naacl-main)
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| Challenge: | Existing fact checking systems that perform well on colloquial claims significantly degenerate on collotic claims with the same semantics. |
| Approach: | They propose to transfer the styles of claims from FEVER into colloquialism to investigate fact checking systems on colloqual claims. |
| Outcome: | The proposed system significantly degenerates on colloquial claims with the same semantics. |
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification (2024.eacl-long)
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| Challenge: | Existing systems for fact-checking scientific claims assume that the documents containing the evidence are already provided and annotated or contained in a limited corpus. |
| Approach: | They perform an array of experiments to test the performance of open-domain claim verification systems on four datasets of biomedical and health claims in different settings. |
| Outcome: | The proposed system performs better with biomedical and health claims, while Wikipedia is more suited for everyday health concerns. |
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval (2025.emnlp-industry)
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Ahmed Masry, Megh Thakkar, Patrice Bechard, Sathwik Tejaswi Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte Suresh, Srivatsava Daruru, Enamul Hoque, Spandana Gella, Torsten Scholak, Sai Rajeswar
| Challenge: | Existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval. |
| Approach: | They propose a document retrieval model that bridges the gap between multimodal representation learning and document retrievals by providing external knowledge as context. |
| Outcome: | The proposed model achieves 3.61% improvement over existing retrieval models on the ViDoRe V2 benchmark, showing stronger generalization to out-of-domain benchmarks. |
Dating Documents using Graph Convolution Networks (P18-1)
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| Challenge: | Existing approaches for document dating assume accurate knowledge of document date, but this is not always available for arbitrary documents from the Web. |
| Approach: | They propose a Graph Convolutional Network (GCN) based document dating approach which exploits syntactic and temporal graph structures of document in a principled way. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets by 19% absolute accuracy points. |
Large Language Models are Built-in Autoregressive Search Engines (2023.findings-acl)
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| Challenge: | Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing only shallow interactions between them. |
| Approach: | They propose to use large language models to generate URLs for document retrieval by following human instructions. |
| Outcome: | The proposed method achieves better retrieval performance than existing retrieval approaches on open-domain question answering benchmarks. |
Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective (2025.emnlp-main)
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| Challenge: | Large language model (LLM)-based embedding models surpass BERT and T5 on general-purpose text embeddable tasks. |
| Approach: | They propose to adopt diffusion language models for text embeddings to overcome limitations in unidirectional attention used during autoregressive pre-training. |
| Outcome: | The proposed model outperforms the existing LLM-based embedding model on reasoning tasks by 20% and 2% on traditional embeddable benchmarks. |
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification (N19-1)
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| Challenge: | Existing fact-checking datasets do not provide manual annotations for sentence-level evidence. |
| Approach: | They propose a task-agnostic pipelined system that extracts textual evidence that supports or refutes a factual claim from Wikipedia pages. |
| Outcome: | The proposed system achieves state-of-the-art results on the FEVER dataset. |
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) excel in zero-shot document ranking tasks. |
| Approach: | They propose a prompt-based re-ranking method that requires no further training but is only feasible for reranking a handful of candidates due to computational costs. |
| Outcome: | The proposed method can retrieve documents from the entire corpus without training and with a large amount of paired text data. |
Test-time Corpus Feedback: From Retrieval to RAG (2026.findings-eacl)
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| Challenge: | Retrieval-augmented generation (RAG) pipelines treat retrieval and reasoning as isolated components, limiting performance on complex tasks. |
| Approach: | They propose to integrate large language models with retrieval to improve query quality . they also propose to use feedback to improve the query, retrieved context, or document pool . |
| Outcome: | The proposed methods bridge IR and NLP perspectives and highlight retrieval as a dynamic, learnable component of end-to-end RAG systems. |
A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections (2021.acl-long)
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| Challenge: | Question answering systems typically use pipelines that retrieve documents at finer text granularities. |
| Approach: | They propose an architecture for document and snippet ranking that leverages intuition . they modified a natural questions dataset to test their model . |
| Outcome: | The proposed model outperforms pipelines in document retrieval on biomedical data . the proposed model is competitive with the existing model, despite fewer parameters . |
Query Decomposition for RAG: Balancing Exploration-Exploitation (2026.eacl-long)
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Roxana Petcu, Kenton Murray, Daniel Khashabi, Evangelos Kanoulas, Maarten de Rijke, Dawn Lawrie, Kevin Duh
| Challenge: | Complex user queries often involve the exclusion of information, negation, or missing entities. |
| Approach: | They propose to decompose user requests into subqueries, retrieve potentially relevant documents for each and then aggregate them to generate an answer. |
| Outcome: | The proposed method achieves 35% gain in document-level precision and 15% increase in -nDCG . it also improves the downstream task of long-form generation. |
Adversarial Semantic Collisions (2020.emnlp-main)
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| Challenge: | Existing approaches to generate semantic collisions for NLP tasks are vulnerable to adversarial examples. |
| Approach: | They propose gradient-based approaches for generating semantic collisions given white-box access to a model and deploy them against several NLP tasks. |
| Outcome: | The proposed approaches evade perplexity-based filtering and discuss other potential mitigations. |
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models (2025.naacl-long)
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| Challenge: | Recent surge in multilingual large language models (LLMs) and Retrieval Augmented Generation (RAG) has significantly expanded conversational search across varied linguistic and cultural demographics. |
| Approach: | They found that LLMs displayed systemic bias towards information in the same language as query language in document retrieval and answer generation. |
| Outcome: | The results highlight the linguistic divide within multilingual LLMs in information search systems. |
CHARM: Inferring Personal Attributes from Conversations (2020.emnlp-main)
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| Challenge: | Personal Knowledge Bases (PKBs) capture individual user traits for customizing downstream applications like chatbots or recommenders. |
| Approach: | They propose a method that leverages keyword extraction and document retrieval to predict attribute values that were never seen during training. |
| Outcome: | The proposed method can predict attributes that were never seen during training. |
Auto Search Indexer for End-to-End Document Retrieval (2023.findings-emnlp)
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| Challenge: | Generative retrieval heavily relies on the “preprocessed” document identifiers, thus limiting its retrieval performance and ability to retrieve new documents. |
| Approach: | They propose a fully end-to-end retrieval paradigm that can learn the best docids for existing and new documents automatically via a semantic indexing module. |
| Outcome: | The proposed model outperforms baselines on public and industrial datasets and can handle new documents. |
GLEN: Generative Retrieval via Lexical Index Learning (2023.emnlp-main)
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| Challenge: | Existing methods for document retrieval bypass auxiliary index structures and can be optimized through end-to-end learning. |
| Approach: | They propose a method to generate a relevant document's identifier using an index learning strategy. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on benchmark datasets. |
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)
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| Challenge: | Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim . |
| Approach: | They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents. |
| Outcome: | The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems. |
MixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity (2024.emnlp-main)
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| Challenge: | Recent studies show the importance of document retrieval in the scientific domain. |
| Approach: | They propose a zero-shot approach to measure query-document similarity using atomic components in queries and documents to combine them into a united score. |
| Outcome: | The proposed approach outperforms previous document retrieval methods by 24.7%, 9.8%, and 6.9% on nDCG@5 with unsupervised, supervised, and LLM-based retrievers. |
Neuro-Symbolic Query Compiler (2025.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems are limited in their ability to process information in open-source environments. |
| Approach: | They propose a neuro-symbolic framework inspired by linguistic grammar rules and compiler design to formalize complex queries using a minimal yet sufficient Backus-Naur Form grammar. |
| Outcome: | The proposed framework is based on a backus-naur form grammar and compiler design that maintains completeness while minimizing redundancy. |
FoodSafeSum: Enabling Natural Language Processing Applications for Food Safety Document Summarization and Analysis (2025.findings-emnlp)
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Juli Bakagianni, Korbinian Randl, Guido Rocchietti, Cosimo Rulli, Franco Maria Nardini, Salvatore Trani, Aron Henriksson, Anna Romanova, John Pavlopoulos
| Challenge: | a lack of structured datasets hinders natural language processing research . a new dataset of food safety documents and related metadata is presented . |
| Approach: | They present a dataset of human-written and Large Language Model (LLM)-generated food safety documents . they evaluate their utility on three NLP tasks directly reflecting food safety practices . |
| Outcome: | The proposed dataset performs comparably or better than human summaries on three NLP tasks . it also shows clustering of summary for event tracking and compliance monitoring . |
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)
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| Challenge: | Experimental results show that the main challenge lies in long context and perspective extraction. |
| Approach: | They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline . |
| Outcome: | The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform . |
Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Extensive experiments on widely used benchmarks demonstrate the efficacy of our approach, yielding a 10.6% accuracy improvement on HotpotQA. |
| Approach: | They propose a Lexical Diversity-aware RAG method to address the biases in relevant information retrieval and utilization induced by lexical diversity. |
| Outcome: | Extensive experiments on widely used benchmarks show the proposed method yields a 10.6% accuracy improvement on HotpotQA. |
Detecting Legal Citations in United Kingdom Court Judgments (2025.emnlp-main)
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| Challenge: | citation detection in court judgments is challenging because of the complexity of legal language . citation analysis is critical for many legal applications, but the complexity is not always easy to solve. |
| Approach: | They compare three different models for citation detection in court judgments using the Cambridge Law Corpus . they compare rulebased regular expressions, transformer-based encoders and large language models . |
| Outcome: | The proposed model outperforms the existing models in the citation analysis and analysis of 190 court judgments. |
Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval (2026.findings-acl)
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| Challenge: | Large language models have demonstrated that explicit step-by-step thinking can substantially improve performance on complex tasks. |
| Approach: | They propose a model that generates preliminary thoughts for input queries before document retrieval. |
| Outcome: | The proposed model generates preliminary thoughts for input queries before document retrieval. |