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Passage Retrieval
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Passage retrieval is a specialized type of IR application that retrieves relevant passages (or pieces of text) rather than an entire ranked set of documents.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/simxns
공식 코드MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers
https://github.com/microsoft/unilm
공식 코드SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
https://github.com/miulab/convadr-qa
공식 코드Open-Domain Conversational Question Answering with Historical Answers
https://github.com/morningmoni/GAR
공식 코드Generation-Augmented Retrieval for Open-domain Question Answering
https://github.com/muennighoff/sgpt
공식 코드https://github.com/navid-rekabsaz/GenderBias_IR
공식 코드https://github.com/nickvosk/sigir2020-query-resolution
공식 코드Query Resolution for Conversational Search with Limited Supervision
https://github.com/noewangjy/csprd_dataset
공식 코드CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market
https://github.com/nyu-dl/dl4marco-bert
공식 코드https://github.com/paddlepaddle/rocketqa
공식 코드https://github.com/prdwb/okvqa-release
공식 코드Passage Retrieval for Outside-Knowledge Visual Question Answering
https://github.com/princeton-nlp/DensePhrases
공식 코드https://github.com/princeton-nlp/entityquestions
공식 코드https://github.com/rmahari/lepard
공식 코드https://github.com/ruyue0001/backdoor_dpr
공식 코드Whispers in Grammars: Injecting Covert Backdoors to Compromise Dense Retrieval Systems
https://github.com/sebastian-hofstaetter/sigir19-neural-ir
공식 코드https://github.com/shashank140195/Biomedical_QA_Model
공식 코드Top K Relevant Passage Retrieval for Biomedical Question Answering
https://github.com/slanglab/IndiaPoliceEvents
공식 코드https://github.com/sophiaalthammer/dossier_coliee
공식 코드https://github.com/sophiaalthammer/parm
공식 코드PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
https://github.com/stanford-futuredata/ColBERT
공식 코드ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
https://github.com/starsuzi/dar
공식 코드Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation
https://github.com/studio-ousia/bpr
공식 코드Efficient Passage Retrieval with Hashing for Open-domain Question Answering
https://github.com/svakulenk0/cast_evaluation
공식 코드A Comparison of Question Rewriting Methods for Conversational Passage Retrieval
https://github.com/tangzhy/dptdr
공식 코드https://github.com/taoshen58/lexmae
공식 코드LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
https://github.com/terrierteam/pyterrier_colbert
공식 코드On Single and Multiple Representations in Dense Passage Retrieval
https://github.com/thuir/t2ranking
공식 코드T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
https://github.com/thunlp/fastfid
공식 코드FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
https://github.com/ukplab/acl2024-dapr
공식 코드https://github.com/ukplab/arxiv2023-dapr
공식 코드https://github.com/ukplab/peerqa
공식 코드PeerQA: A Scientific Question Answering Dataset from Peer Reviews
https://github.com/umanlp/zusammenqa
공식 코드https://github.com/ustc-starteam/genki
공식 코드https://github.com/voidism/ear
공식 코드Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.