I am doing a PhD on Information retriever and I am happy to share what I learning All what you need for information retrieval resources. (Inspired by caiyinqiong)
Pipeline IR - How to build an IR pipeline
Feel free to participate. Work in progress...
- Survey Paper
- Query Expansion/Query-ReWrite in progress...
- Retieval
- Chapter 4: Other Resources
- Vector DataBase to do...
- Metrics to do...
- Unsupervised domain adaptation to do...
- Datasets to do...
- Indexing Methods to do...
- Packages to do...
- Reader to do...
- Conferences to do...
| Paper | Author | Venue | Code |
|---|---|---|---|
| Semantic Matching in Search | Li et al., 2014 | NA | NA |
| Pretrained Transformers for Text Ranking: BERT and Beyond | Lin et al., 2021 | arXiv | NA |
| Semantic Models for the First-stage Retrieval: A Comprehensive Review | Guo et al., 2021 | TOIS | |
| A Proposed Conceptual Framework for a Representational Approach to Information Retrieval | Lin et al., 2021 | arXiv | NA |
| Pre-training Methods in Information Retrieval | Fan et al., 2022 | arXiv | |
| Dense Text Retrieval based on Pretrained Language Models: A Survey | Zhao et al., 2022 | arXiv | |
| Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey | Shen et al., 2022 | arXiv | |
| Lecture Notes on Neural Information Retrieval | Tonellotto et al., 2022 | arXiv |
| Paper | Author | Year | Model |
|---|---|---|---|
| Word-word Associations in Document Retrieval Systems | NA | 1969 | NA |
| Concept based Query Expansion | NA | 1993 | NA |
| Query Expansion using Lexical-Semantic Relations | NA | 1994 | NA |
| Using Query Contexts in Information Retrieval | NA | 2007 | NA |
| Paper | Author | Year | Model |
|---|---|---|---|
| Relevance Feedback in Information Retrieval | NA | 1971 | Rocchio PRF |
| Model-based Feedback in the Language Modeling Approach to Information Retrieval | NA | 2001 | Divergence Minimization Model |
| UMass at TREC 2004: Novelty and HARD | NA | 2004 | RM3 for PRF |
| Selecting Good Expansion Terms for Pseudo-Relevance Feedback | NA | 2008 | PRF |
| A Comparative Study of Methods for Estimating Query Language Models with Pseudo Feedback | NA | 2009 | NA |
| Pseudo-Relevance Feedback Based on Matrix Factorization | NA | 2016 | NA |
| Reducing the Risk of Query Expansion via Robust Constrained Optimization | NA | 2009 | query drift problem |
| Query Expansion using Local and Global Document Analysis | NA | 2017 | NA |
| Paper | Author | Year | Model |
|---|---|---|---|
| A Vector Space Model for Automatic Indexing | NA | 1975 | VSM |
| Developments in Automatic Text Retrieval | NA | 1991 | TFIDF |
| Term-weighting Approaches in Automatic Text Retrieval | NA | 1988 | TFIDF |
| Relevance Weighting of Search Terms | NA | 1976 | BIM |
| A Theoretical Basis for the Use of Co-occurrence Data in Information Retrieval | NA | 1997 | Tree Dependence Model |
| The Probabilistic Relevance Framework: BM25 and Beyond | NA | 2010 | BM25 |
| A Language Modeling Approach to Information Retrieval | NA | 1998 | QL |
| Statistical Language Models for Information Retrieval | NA | 2007 | LM for IR |
| Hypergeometric Language Model and Zipf-Like Scoring Function for Web Document Similarity Retrieval | NA | 2010 | LM for IR |
| Probabilistic Models of Information Retrieval Based on Measuring the Divergence from Randomness | NA | 2002 | DFR |
| Paper | Year | Model |
|---|---|---|
| Experiments in Automatic Phrase Indexing For Document Retrieval: A Comparison of Syntactic and Non-Syntactic Methods | 1987 | VSM + term dependency |
| Term-weighting Approaches in Automatic Text Retrieval | 1988 | VSM + term dependency |
| An Analysis of Statistical and Syntactic Phrases | 1997 | VSM + term dependency |
| A Probabilistic Model of Information Retrieval: Development and Comparative Experiments | 2000 | VSM + term dependency |
| Relevance Ranking Using Kernels | 2010 | BM25 + term dependency |
| A General Language Model for Information Retrieval | 1999 | LM + term dependency |
| Biterm Language Models for Document Retrieval | 2002 | LM + term dependency |
| Capturing Term Dependencies using a Language Model based on Sentence Trees | 2002 | LM + term dependency |
| Dependence Language Model for Information Retrieval | 2004 | LM + term dependency |
| A Generative Theory of Relevance | 2008 | NA |
| A Markov Random Field Model for Term Dependencies | 2005 | SDM |
| Paper | Year | Author | Code |
|---|---|---|---|
| Corpus Structure, Language Models, and Ad Hoc Information Retrieval | 2004 | Susan T. Dumais | NA |
| Cluster-Based Retrieval Using Language Models | 2004 | Susan T. Dumais | NA |
| Language Model Information Retrieval with Document Expansion | 2006 | Jimmy Lin | NA |
| Document Expansion Based on WordNet for Robust IR | 2010 | Jey Han Lau | NA |
| Improving Retrieval of Short Texts Through Document Expansion | 2012 | Hang Li | NA |
| Document Expansion Using External Collections | 2017 | Hui Fang | WordNet-based |
| Document Expansion versus Query Expansion for Ad-hoc Retrieval | 2005 | Douglas W. Oard | NA |
| Paper | Author | Year | Code |
|---|---|---|---|
| Information Retrieval as Statistical Translation | Bonnie J. Dorr | 1999 | NA |
| Estimation of Statistical Translation Models Based on Mutual Information for Ad Hoc Information Retrieval | Benjamin Van Durme | 2010 | NA |
| Clickthrough-Based Translation Models for Web Search: From Word Models to Phrase Models | Hang Li | 2010 | NA |
| Axiomatic Analysis of Translation Language Model for Information Retrieval | ChengXiang Zhai | 2012 | NA |
| Query Rewriting Using Monolingual Statistical Machine Translation | Maosong Sun | 2010 | for query expansion |
| Towards Concept-based Translation Models using Search Logs for Query Expansion | Zhen Tan | 2012 | for query expansion |
-
Term Re-weighting | Paper | Author | Year | Conference/Journal | Code | | --- | --- | --- | --- | --- | | Learning to Reweight Terms with Distributed Representations | Zheng et al. | 2015 | SIGIR | DeepTR | | Integrating and Evaluating Neural Word Embeddings in Information Retrieval | Zuccon et al. | 2015 | ADCS | NTLM | | Learning Term Discrimination | Frej et al. | 2020 | SIGIR | TDV | | Context-Aware Sentence/Passage Term Importance Estimation for First Stage Retrieval | Dai et al. | 2019 | arXiv | DeepCT | | Context-Aware Term Weighting For First-Stage Passage Retrieval | Dai et al. | 2020 | SIGIR | DeepCT | | Efficiency Implications of Term Weighting for Passage Retrieval | Mackenzie et al. | 2020 | SIGIR | DeepCT | | Context-Aware Document Term Weighting for Ad-Hoc Search | Dai et al. | 2020 | WWW | HDCT | | A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques | Lin et al. | 2021 | arXiv | uniCOIL |
-
Expansion
- Document Expansion by query Prediction（Nogueira et al., 2019, arXiv, Doc2Query）
- From doc2query to docTTTTTquery（Nogueira et al., 2019, arXiv, DocTTTTTQuery）
- A Unified Pretraining Framework for Passage Ranking and Expansion（Yan et al., 2021, AAAI, UED）
- Generation-augmented Retrieval for Open-domain Question Answering（Mao et al., 2020, ACL, GAR, query expansion）
-
Expansion + Term Re-weighting
- Expansion via Prediction of Importance with Contextualization（MacAvaney et al., 2020, SIGIR, EPIC）
- SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval（Bai et al., 2020, arXiv, SparTerm）
- SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking（Formal et al., 2021, SIGIR, SPLADE）
- SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval（Formal et al., 2021, arXiv, SPLADEv2）
- Learning Passage Impacts for Inverted Indexes（Mallia et al., 2021, SIGIR, DeepImapct）
- TILDE: Term Independent Likelihood moDEl for Passage Re-ranking（Zhuang et al., 2021, SIGIR, TILDE）
- Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion（Zhuang et al., 2021, arXiv, TILDEv2）
- SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval（Choi et al., 2022, CIKM）
-
Sparse Representation Learning
- Semantic Hashing（Salakhutdinov et al., 2009）
- From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing（Zamani et al., 2018, CIKM, SNRM）
- UHD-BERT: Bucketed Ultra-High Dimensional Sparse Representations for Full Ranking（Jang et al., 2021, arXiv, UHD-BERT）
- Efficient Passage Retrieval with Hashing for Open-domain Question Answering（Yamada et al., 2021, ACL, BPR）
- Composite Code Sparse Autoencoders for First Stage Retrieval（Lassance et al., 2021, SIGIR, CCSA）
- Word-Embedding-based
- Aggregating Continuous Word Embeddings for Information Retrieval（Clinchant et al., 2013, ACL, FV）
- Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings（Vulic et al., 2015, SIGIR）
- Short Text Similarity with Word Embeddings（Kenter et al., 2015, CIKM, OoB）
- A Dual Embedding Space Model for Document Ranking（Mitra et al., 2016, arXiv, DESM）
- Efficient Natural Language Response Suggestion for Smart Reply（Henderson et al., 2017, arXiv）
- End-to-End Retrieval in Continuous Space（Gillick et al., 2018, arXiv）
- Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension（Seo et al., 2018, EMNLP, PIQA）
- Dense Passage Retrieval for Open-Domain Question Answering（Karpukhin et al., 2020, EMNLP, DPR）
- Retrieval-augmented generation for knowledge-intensive NLP tasks（Lewis et al., 2020, NIPS, RAG）
- RepBERT: Contextualized Text Embeddings for First-Stage Retrieval（Zhan et al., 2020, arXiv, RepBERT）
- CoRT: Complementary Rankings from Transformers（Wrzalik et al., 2020, NAACL, CoRT）
- DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding（Nie et al., 2020, SIGIR, DC-BERT）
- Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation（Yang et al., 2021, ACL, data augmentation）
- Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval（Xiong et al., 2020, arXiv, ANCE）
- Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently（Zhan et al., 2020, arXiv, LTRe）
- GLOW : Global Weighted Self-Attention Network for Web（Shan et al, 2020, arXiv, GLOW）
- An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering（Qu et al., 2021, ACL, RocketQA）
- Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling（Hofstätter et al., 2021, SIGIR, TAS-Balanced）
- Optimizing Dense Retrieval Model Training with Hard Negatives（Zhan et al., 2021, SIGIR, STAR/ADORE）
- Few-Shot Conversational Dense Retrieval（Yu et al., 2021, SIGIR）
- Learning Dense Representations of Phrases at Scale（Lee et al., 2021, ACL, DensePhrases）
- More Robust Dense Retrieval with Contrastive Dual Learning（Lee et al., 2021, ICTIR, DANCE）
- PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval（Ren et al., 2021, ACL, PAIR）
- Relevance-guided Supervision for OpenQA with ColBERT（Khattab et al., 2021, TACL, ColBERT-QA）
- End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering（Sachan et al., 2021, arXiv, EMDR^2）
- Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback（Yu et al, 2021, CIKM, ANCE-PRF）
- Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval（Wang et al., 2021, ICTIR, ColBERT-PRF）
- A Discriminative Semantic Ranker for Question Retrieval（Cai et al., 2021, ICTIR, DenseTrans）
- Representation Decoupling for Open-Domain Passage Retrieval（Wu et al., 2021, arXiv）
- RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking（Ren et al., 2021, EMNLP, RocketQAv2）
- Efficient Training of Retrieval Models Using Negative Cache（Lindgren et al., 2021, NeurIPS）
- Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval（Lu et al., 2021, EMNLP）
- Improving Embedding-based Large-scale Retrieval via Label Enhancement（Liu et al., 2021, EMNLP）
- Dense Hierarchical Retrieval for Open-Domain Question Answering（Liu et al., 2021, EMNLP）
- Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval（Xiao er al., 2022，WWW）
- LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval（Zhang et al., 2023, WWW）
- Aggretriever: A Simple Approach to Aggregate Textual Representation for Robust Dense Passage Retrieval（Lin et al., 2022, arXiv）
- Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval（Wu et al., 2022, ACL）
- Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering（Cheng et al., 2022, arXiv）
- Knowledge Distillation
- Distilling Dense Representations for Ranking using Tightly-Coupled Teachers（Lin et al., 2020, arXiv, TCT-ColBERT）
- Distilling Knowledge for Fast Retrieval-based Chat-bots（Tahami et al., 2020, SIGIR）
- Distilling Knowledge from Reader to Retriever for Question Answering（Izacard et al., 2020, arXiv）
- Is Retriever Merely an Approximator of Reader?（Yang et al., 2020, arXiv）
- Improving Bi-encoder Document Ranking Models with Two Rankers and Multi-teacher Distillation（Choi et al., 2021, SIGIR, TRMD）
- Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation（Hofstätter et al., 2021, arXiv, Margin-MSE loss）
- Twinbert: Distilling knowledge to twin-structured compressed bert models for large-scale retrieval（Lu et al., 2022, arXiv）
- Multi-vector Representation
- Multi-Hop Paragraph Retrieval for Open-Domain Question Answering（Feldman et al., 2019, ACL, MUPPET）
- Sparse, Dense, and Attentional Representations for Text Retrieval（Luan et al., 2020, TACL, ME-BERT）
- ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT（Khattab et al., 2020, SIGIR, ColBERT）
- COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List（Gao et al., 2021, NACL, COIL）
- Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval（Tang et al., 2021, ACL）
- Phrase Retrieval Learns Passage Retrieval, Too（Lee et al., 2021, EMNLP, DensePhrases）
- Query Embedding Pruning for Dense Retrieval（Tonellotto et al., 2021, CIKM）
- Multi-View Document Representation Learning for Open-Domain Dense Retrieval（Zhang et al., 2022, ACL）
- ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction（Santhanam, 2022, NAACL）
- Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval（Li et al., 2022, arXiv）
- Topic-Grained Text Representation-based Model for Document Retrieval（Du et al., 2022, ICANN）
- Accelerate Interaction-based Models
- Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks（Mitra et al., 2019, arXiv）
- Efficient Interaction-based Neural Ranking with Locality Sensitive Hashing（Ji et al., 2019, WWW）
- Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring（Humeau et al., 2020, ICLR, Poly-encoders）
- Modularized Transfomer-based Ranking Framework（Gao et al., 2020, EMNLP, MORES）
- Efficient Document Re-Ranking for Transformers by Precomputing Term Representations（MacAvaney et al., 2020, SIGIR, PreTTR）
- DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering（Cao et al., 2020, ACL, DeFormer）
- SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval（Zhao et al., 2020, arXiv, SPARTA）
- Conformer-Kernel with Query Term Independence for Document Retrieval（Mitra et al., 2020, arXiv）
- IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System（Li et al., 2022, CIKM）
- Pre-training
- Latent Retrieval for Weakly Supervised Open Domain Question Answering（Lee et al., 2019, ACL, ORQA）
- Retrieval-Augmented Language Model Pre-Training（Guu et al., 2020, ICML, REALM）
- Pre-training Tasks for Embedding-based Large-scale Retrieval（Chang et al., 2020, ICLR, BFS+WLP+MLM）
- Is Your Language Model Ready for Dense Representation Fine-tuning?（Gao et al., 2021, EMNLP, Condenser）
- Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval（Gao et al., 2021, arXiv, coCondenser）
- Less is More: Pre-training a Strong Siamese Encoder Using a Weak Decoder（Lu et al., 2021, EMNLP, SEED-Encoder）
- Pre-trained Language Model for Web-scale Retrieval in Baidu Search（Liu et al., 2021, KDD）
- Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need（Ma et al., 2021, CIKM, HARP）
- Pre-train a discriminative text encoder for dense retrieval via contrastive span prediction（Ma et al., 2022, SIGIR）
- TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning（Wang et al., 2021, EMNLP）
- ConTextual Mask Auto-Encoder for Dense Passage Retrieval（Wu et al., 2022, arXiv）
- SimLM: Pre-training with representation bottleneck for dense passage retrieval（Wang et al., 2022, arXiv）
- LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval（Shen et al., 2022, arXiv）
- A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval（Ma et al., 2022, CIKM）
- RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder（Xiao and Liu et al., 2022, EMNLP）
- Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrieval（Long et al., 2022, arXiv）
- LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval（Xu et al., 2022, ACL）
- Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation（Liu et al., 2022, arXiv）
- MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers（Zhou et al., 2023, ICLR）
- CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval（Wu et al., 2023, arXiv）
- CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval（Ma et al., 2023, arXiv）
- Joint Learning with Index
- Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index（Zhang et al., 2021, SIGIR, Poeem）
- Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance（Zhan et al., 2021, 8000 CIKM, JPQ）
- Matching-oriented Product Quantization For Ad-hoc Retrieval（Xiao et al., 2021, EMNLP, MoPQ）
- Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval（Zhan et al, 2022, WSDM, RepCONC）
- Joint Learning with Ranker
- End-to-End Training of Neural Retrievers for Open-Domain Question Answering（Sachan et al., 2021, ACL）
- Adversarial Retriever-Ranker for dense text retrieval（Zhang et al., 2022, ICLR）
- Debias
- Learning Robust Dense Retrieval Models from Incomplete Relevance Labels（Prakash et al., 2021, SIGIR, RANCE）
- Hard negatives or false negatives: Correcting pooling bias in training neural ranking models（Cai et al., 2022, CIKM）
- SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval（Zhou et al., 2022, EMNLP）
- Debiased contrastive learning of unsupervised sentence representations（Zhou et al., 2022, ACL）
- Recovering Gold from Black Sand: Multilingual Dense Passage Retrieval with Hard and False Negative Samples（Shen et al., 2022, EMNLP）
- Zero-shot/Few-shot
- Embedding-based Zero-shot Retrieval through Query Generation（Liang et al., 2020, arXiv）
- Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation（Ma et al., 2020, QGen, arXiv）
- Towards Robust Neural Retrieval Models with Synthetic Pre-Training（Reddy et al., 2021, arXiv）
- BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models（Thakur et al., 2021, NeurIPS）
- Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations（Xin et al., 2021, arXiv, MoDIR）
- Large Dual Encoders Are Generalizable Retrievers（Ni et al., 2022, EMNLP, DTR）
- Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models（Chen et al., 2022, ECIR）
- InPars: Data Augmentation for Information Retrieval using Large Language Models（Bonifacio et al., 2022, arXiv）
- Towards Unsupervised Dense Information Retrieval with Contrastive Learning（Izacard et al., 2021, arXiv, Contriever）
- GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval（Wang et al., 2022, NAACL）
- Learning to Retrieve Passages without Supervision（Ram et al., 2021, arXiv, Spider）
- A thorough examination on zero-shot dense retrieval（Ren et al., 2022, arXiv）
- Text and Code Embeddings by Contrastive Pre-Training（Neelakantan et al., 2022, arXiv）
- Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval（Zhan et al., 2022, arXiv）
- Promptagator: Few-shot Dense Retrieval From 8 Examples（Dai et al., 2022, arXiv）
- Questions Are All You Need to Train a Dense Passage Retriever（Sachan et al., 2022, TACL）
- HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization（Cai et al., 2023, ICLR）
- COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning（Yu et al., 2022, EMNLP）
- Challenges in generalization in open domain question answering（Liu et al., 2022, NAACL）
- Robustness
- Towards robust dense retrieval via local ranking alignment（Chen et al., 2022, IJCAI）
- Dealing with typos for bert-based passage retrieval and ranking（Zhuang et al., 2021, EMNLP）
- Evaluating the robustness of retrieval pipelines with query variation generators（Penha et al., 2022, ECIR）
- Analysing the robustness of dual encoders for dense retrieval against misspellings（Sidiropoulos et al., 2022, SIGIR）
- Characterbert and self-teaching for improving the robustness of dense retrievers on queries with typos（Zhuang et al., 2022, SIGIR）
- BERT rankers are brittle: A study using adversarial document perturbations（Wang et al., 2022, ICTIR）
- Order-disorder: Imitation adversarial attacks for blackbox neural ranking models（Liu et al., 2022, arXiv）
- Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval（Zhuang et al., 2023, arXiv）
- Probing Analysis
- The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes（Reimers et al., 2021, ACL）
- Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval（Ma et al., EMNLP, 2021, redundancy）
- BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models（ Thakur et al., 2021, NeurIPS, transferability）
- Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?（Chen et al., 2021, arXiv）
- Simple Entity-Centric Questions Challenge Dense Retrievers（Sciavolino et al., 2021, EMNLP）
- Interpreting Dense Retrieval as Mixture of Topics（Zhan et al., 2021, arXiv）
- An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering（Li et al., 2022, TrustNLP）
- Isotropic Representation Can Improve Dense Retrieval（Jung et al., 2022, arXiv）
- Prompt Learning
- Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning（Jung et al., 2022, WWW）
- Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval（Ma et al., 2022, CIKM）
- DPTDR: Deep Prompt Tuning for Dense Passage Retrieval（Tang et al., 2022, arXiv）
- Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers（Tam et al., 2022, arXiv）
- NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework（Xu et al., 2022, arXiv）
- Large Language Model for Retrieval
- Precise Zero-Shot Dense Retrieval without Relevance Labels（Gao et al., 2022, arXiv）
- Precise Zero-Shot Dense Retrieval without Relevance Labels（Gao et al., 2022, arXiv）
- Others
- HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking（Zhang et al., 2022, arXiv）
- Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training（Zhuang et al., 2022, SIGIR）
- Word-Embedding-based
- Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings（Vulic et al., 2015, SIGIR, linearly combine）
- Word Embedding based Generalized Language Model for Information Retrieval（Ganguly et al., 2015, SIGIR, GLM）
- Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval（Roy et al., 2016, SIGIR, linearly combine）
- A Dual Embedding Space Model for Document Ranking（Mitra et al., 2016, WWW, DESM_mixture, linearly combine）
- Off the Beaten Path: Let’s Replace Term-Based Retrieval with k-NN Search（Boytsov et al., 2016, CIKM, BM25+translation model）
- Learning Hybrid Representations to Retrieve Semantically Equivalent Questions（Santos et al., 2015, ACL, BOW-CNN）
- Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index （Seo et al., 2019, ACL, DenSPI）
- Contextualized Sparse Representations for Real-Time Open-Domain Question Answering（Lee et al., 2020, ACL, SPARC）
- CoRT: Complementary Rankings from Transformers（Wrzalik et al., 2020, NAACL, CoRT_BM25）
- Sparse, Dense, and Attentional Representations for Text Retrieval（Luan et al., 2020, TACL, ME-Hybrid）
- Complement Lexical Retrieval Model with Semantic Residual Embeddings（Gao et al., 2020, ECIR, CLEAR）
- Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach（Kuzi et al., 2020, arXiv, Hybrid）
- A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques（Lin et al., 2021, arXiv, uniCOIL）
- Contextualized Offline Relevance Weighting for Efficient and Effective Neural Retrieval（Chen et al., 2021, SIGIR）
- Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection（Arabzadeh et al., 2021, CIKM）
- Fast Forward Indexes for Efficient Document Ranking（Leonhardt et al., 2021, arXiv）
- Densifying Sparse Representations for Passage Retrieval by Representational Slicing（Lin et al., 2021, arXiv）
- UnifieR: A Unified Retriever for Large-Scale Retrieval（Shen et al., 2022, arXiv）
- E-commerce Search
- Deep Interest Network for Click-Through Rate Prediction（Zhou et al., 2018, KDD, DIN）
- From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search（Li et al., 2019, SIGIR, Jingdong）
- Multi-Interest Network with Dynamic Routing for Recommendation at Tmall（Li et al., 2019, CIKM, MIND, Tmall）
- Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning（Zhang et al., 2020, SIGIR, DPSR, Jingdong）
- Deep Multi-Interest Network for Click-through Rate Prediction（Xiao et al., 2020, CIKM, DMIN）
- Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations（Gao et al., 2020, arXiv）
- Embedding-based Product Retrieval in Taobao Search（Li et al., 2021, KDD, taobao）
- Embracing Structure in Data for Billion-Scale Semantic Product Search（Lakshman et al., 2021, arXiv, Amazon）
- Sponsored Search
- MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search（Fan et al., 2019, KDD, Baidu）
- Image Retrieval
- Binary Neural Network Hashing for Image Retrieval（Zhang et al., 2021, SIGIR, BNNH）
- Deep Self-Adaptive Hashing for Image Retrieval（Lin et al., 2021, CIKM, DSAH）
- Report on the First HIPstIR Workshop on the Future of Information Retrieval（Dietz et al., 2019, SIGIR, workshop）
- Let’s measure run time! Extending the IR replicability infrastructure to include performance aspects（Hofstätter et al., 2019, SIGIR）
- Embedding-based Retrieval in Facebook Search（Huang et al., 2020, KDD, EBR）
- Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations（Chen et al., 2018, ICML）
- 【MS MARCO】MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
- 【TREC CAR】TREC Complex Answer Retrieval Overview
- 【TREC DL】Overview of the TREC 2019 deep learning track
- 【TREC COVID】TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
- Tree-based
- Hashing-based
- Quantization-based
- Product Quantization for Nearest Neighbor Search（2010, PQ）
- Optimized Product Quantization（2013, OPQ）
- Graph-based
- Toolkits
- Faiss: a library for efficient similarity search and clustering of dense vectors
- SPTAG: A library for fast approximate nearest neighbor search
- OpenMatch: An Open-Source Package for Information Retrieval
- Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations
- ElasticSearch