shubhampachori12110095 / Semantic-Retrieval-Models

A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).

Awesome Semantic Models for the First-stage Retrieval

Note:

• A curated list of awesome papers for Semantic Retrieval, including some early methods and recent neural models for information retrieval tasks (e.g., ad-hoc retrieval, open-domain QA, community-based QA, and automatic conversation).
• For researchers who want to acquire semantic models for re-ranking stages, we refer readers to the awesome NeuIR survey by Guo et.al.
• Any feedback and contribution are welcome, please open an issue or contact me.

Contents

• Survey Paper
• Chapter 1: Classical Term-based Retrieval
• Chapter 2: Early Methods for Semantic Retrieval
  • Query Expansion
  • Document Expansion
  • Term Dependency Model
  • Topic Model
  • Translation Model
• Chapter 3: Neural Methods for Semantic Retrieval
  • Sparse Retrieval Methods
  • Dense Retrieval Methods
  • Hybrid Retrieval Methods
• Chapter 4: Other Resources
  • Other Tasks
  • Datasets
  • Indexing Methods

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Survey Paper

• Semantic Matching in Search（Li et al., 2014）
• Pretrained Transformers for Text Ranking: BERT and Beyond（Lin et al., 2021, arXiv）
• 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）
• Pre-training Methods in Information Retrieval（Fan et al., 2022, arXiv）

Classical Term-based Retrieval

• A Vector Space Model for Automatic Indexing（1975, VSM）
• Developments in Automatic Text Retrieval（1991, TFIDF）
• Term-weighting Approaches in Automatic Text Retrieval（1988, TFIDF）
• Relevance Weighting of Search Terms（1976, BIM）
• A Theoretical Basis for the Use of Co-occurrence Data in Information Retrieval（1997, Tree Dependence Model）
• The Probabilistic Relevance Framework: BM25 and Beyond（2010, BM25）
• A Language Modeling Approach to Information Retrieval（1998, QL）
• Statistical Language Models for Information Retrieval（2007, LM for IR）
• Hypergeometric Language Model and Zipf-Like Scoring Function for Web Document Similarity Retrieval（2010, LM for IR）
• Probabilistic Models of Information Retrieval Based on Measuring the Divergence from Randomness（2002, DFR）

Early Methods for Semantic Retrieval

Query Expansion

• Global Model
  • Word-word Associations in Document Retrieval Systems（1969）
  • Concept based Query Expansion（1993）
  • Query Expansion using Lexical-Semantic Relations（1994）
  • Using Query Contexts in Information Retrieval（2007）
• Local Model
  • Relevance Feedback in Information Retrieval（1971, Rocchio PRF）
  • Model-based Feedback in the Language Modeling Approach to Information Retrieval（2001, Divergence Minimization Model）
  • UMass at TREC 2004: Novelty and HARD（2004, RM3 for PRF）
  • Selecting Good Expansion Terms for Pseudo-Relevance Feedback（2008, PRF）
  • A Comparative Study of Methods for Estimating Query Language Models with Pseudo Feedback（2009）
  • Pseudo-Relevance Feedback Based on Matrix Factorization（2016）
  • Reducing the Risk of Query Expansion via Robust Constrained Optimization（2009，query drift problem）
• Query Expansion using Local and Global Document Analysis（2017）

Document Expansion

• Corpus Structure, Language Models, and Ad Hoc Information Retrieval（2004）
• Cluster-Based Retrieval Using Language Models（2004）
• Language Model Information Retrieval with Document Expansion（2006）
• Document Expansion Based on WordNet for Robust IR（2010）
• Improving Retrieval of Short Texts Through Document Expansion（2012）
• Document Expansion Using External Collections（2017, WordNet-based）
• Document Expansion versus Query Expansion for Ad-hoc Retrieval（2005）

Term Dependency 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）
• A Markov Random Field Model for Term Dependencies（2005, SDM）

Topic Model

• Generalized Vector Space Model In Information Retrieval（1985, GVSM）
• Indexing by Latent Semantic Analysis（1990, LSI for IR）
• Probabilistic Latent Semantic Indexing（2017, PLSA, linearly combine）
• Corpus Structure, Language Models, and Ad Hoc Information Retrieval（2004, smoothing)
• Regularizing Ad Hoc Retrieval Scores（2005, smoothing）
• LDA-Based Document Models for Ad-Hoc Retrieval（2006, LDA for IR and LDA for LM smoothing）
• A Comparative Study of Utilizing Topic Models for Information Retrieval（2009, smoothing）
• Investigating Task Performance of Probabilistic Topic Models: An Empirical Study of PLSA and LDA（2010）
• Latent Semantic Indexing (LSI) Fails for TREC Collections（2011）

Translation Model

• Information Retrieval as Statistical Translation（1999）
• Estimation of Statistical Translation Models Based on Mutual Information for Ad Hoc Information Retrieval（2010）
• Clickthrough-Based Translation Models for Web Search: From Word Models to Phrase Models（2010）
• Axiomatic Analysis of Translation Language Model for Information Retrieval（2012）
• Query Rewriting Using Monolingual Statistical Machine Translation（2010, for query expansion）
• Towards Concept-based Translation Models using Search Logs for Query Expansion（2012, for query expansion）

Neural Methods for Semantic Retrieval

Sparse Retrieval Methods

• Term Re-weighting
  • 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）

Dense Retrieval Methods

• 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）
• Adversarial Retriever-Ranker for dense text retrieval（Zhang et al., 2022, ICLR）
• 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., 2022, arXiv）
• Aggretriever: A Simple Approach to Aggregate Textual Representation for Robust Dense Passage Retrieval（Lin 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）
  • 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）
• 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）
  • ConTextual Mask Auto-Encoder for Dense Passage Retrieval（Wu et al., 2022, arXiv）
  • RetroMAE: Pre-training retrieval-oriented transformers via masked auto-encoder（Liu 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）
• 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, 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）
• Debias
  • Learning Robust Dense Retrieval Models from Incomplete Relevance Labels（Prakash et al., 2021, SIGIR, RANCE）
• 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., 2021, arXiv, 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., 2021, arXiv）
  • Learning to Retrieve Passages without Supervision（Ram et al., 2021, arXiv, Spider）
  • 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）
• 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）
• 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）
• Others
  • Isotropic Representation Can Improve Dense Retrieval（Jung et al., 2022, arXiv）
  • Towards Robust Dense Retrieval via Local Ranking Alignment（Chen et al, 2022, IJCAI）

Hybrid Retrieval Methods

• 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）

Other Resources

Other Tasks

• 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）

Datasets

• 【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

Indexing Methods

• Tree-based
  • Multidimensional Binary Search Trees Used for Associative Searching（1975, KD tree）
  • Annoy
• Hashing-based
  • Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality（1998, LSH）
• Quantization-based
  • Product Quantization for Nearest Neighbor Search（2010, PQ）
  • Optimized Product Quantization（2013, OPQ）
• Graph-based
  • Navigation in a Small World（2000, NSW）
  • Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs（2018, HNSW）
• 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