cluster #9

Papers

• Massively Multilingual Sentence Embeddings For Zero-shot Cross-lingual Transfer And Beyond (2018)
  Mikel Artetxe, Holger Schwenk
  26.33
• Simlm: Pre-training With Representation Bottleneck For Dense Passage Retrieval (2022)
  Liang Wang, Nan Yang, Xiaolong Huang, et al.
  20.27
• IRGAN: A Minimax Game For Unifying Generative And Discriminative Information Retrieval Models (2017)
  Jun Wang, Lantao Yu, Weinan Zhang, et al.
  20.19
• M3-embedding: Multi-linguality, Multi-functionality, Multi-granularity Text Embeddings Through Self-knowledge Distillation (2024)
  Jianlv Chen, Shitao Xiao, Peitian Zhang, et al.
  19.54
• End-to-end Neural Ad-hoc Ranking With Kernel Pooling (2017)
  Chenyan Xiong, Zhuyun Dai, Jamie Callan, et al.
  18.96
• Learning To Match Using Local And Distributed Representations Of Text For Web Search (2016)
  Bhaskar Mitra, Fernando Diaz, Nick Craswell
  18.09
• A Deep Look Into Neural Ranking Models For Information Retrieval (2019)
  Jiafeng Guo, Yixing Fan, Liang Pang, et al.
  17.73
• GPL: Generative Pseudo Labeling For Unsupervised Domain Adaptation Of Dense Retrieval (2021)
  Kexin Wang, Nandan Thakur, Nils Reimers, et al.
  17.47
• Precise Zero-shot Dense Retrieval Without Relevance Labels (2022)
  Luyu Gao, Xueguang Ma, Jimmy Lin, et al.
  17.27
• Efficiently Teaching An Effective Dense Retriever With Balanced Topic Aware Sampling (2021)
  Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, et al.
  17.07
• Neural Ranking Models With Weak Supervision (2017)
  Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, et al.
  17.02
• Few-shot Conversational Dense Retrieval (2021)
  Shi Yu, Zhenghao Liu, Chenyan Xiong, et al.
  16.68
• Deeper -- Deep Entity Resolution (2017)
  Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, et al.
  16.53
• Optimizing Dense Retrieval Model Training With Hard Negatives (2021)
  Jingtao Zhan, Jiaxin Mao, Yiqun Liu, et al.
  16.34
• Query Expansion With Locally-trained Word Embeddings (2016)
  Fernando Diaz, Bhaskar Mitra, Nick Craswell
  16.14
• Relation Extraction As Open-book Examination: Retrieval-enhanced Prompt Tuning (2022)
  Xiang Chen, Lei Li, Ningyu Zhang, et al.
  16.08
• Dense Text Retrieval Based On Pretrained Language Models: A Survey (2022)
  Wayne Xin Zhao, Jing Liu, Ruiyang Ren, et al.
  15.95
• Learning A Deep Listwise Context Model For Ranking Refinement (2018)
  Qingyao Ai, Keping Bi, Jiafeng Guo, et al.
  15.85
• Evaluating Stochastic Rankings With Expected Exposure (2020)
  Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, et al.
  15.83
• Efficient Passage Retrieval With Hashing For Open-domain Question Answering (2021)
  Ikuya Yamada, Akari Asai, Hannaneh Hajishirzi
  15.77
• Medcpt: Contrastive Pre-trained Transformers With Large-scale Pubmed Search Logs For Zero-shot Biomedical Information Retrieval (2023)
  Qiao Jin, Won Kim, Qingyu Chen, et al.
  15.34
• Condenser: A Pre-training Architecture For Dense Retrieval (2021)
  Luyu Gao, Jamie Callan
  14.90
• Mr. Tydi: A Multi-lingual Benchmark For Dense Retrieval (2021)
  Xinyu Zhang, Xueguang Ma, Peng Shi, et al.
  14.80
• Large Dual Encoders Are Generalizable Retrievers (2021)
  Jianmo Ni, Chen Qu, Jing Lu, et al.
  14.69
• Adversarial Sampling And Training For Semi-supervised Information Retrieval (2018)
  Dae Hoon Park, Yi Chang
  14.43
• Less Is More: Pre-train A Strong Text Encoder For Dense Retrieval Using A Weak Decoder (2021)
  Shuqi Lu, di He, Chenyan Xiong, et al.
  14.29
• Semantic Models For The First-stage Retrieval: A Comprehensive Review (2021)
  Jiafeng Guo, Yinqiong Cai, Yixing Fan, et al.
  14.27
• That Is A Known Lie: Detecting Previously Fact-checked Claims (2020)
  Shaden Shaar, Giovanni da San Martino, Nikolay Babulkov, et al.
  14.02
• Table2vec: Neural Word And Entity Embeddings For Table Population And Retrieval (2019)
  Li Deng, Shuo Zhang, Krisztian Balog
  13.55
• Complementing Lexical Retrieval With Semantic Residual Embedding (2020)
  Luyu Gao, Zhuyun Dai, Tongfei Chen, et al.
  13.50
• Large Language Models Are Built-in Autoregressive Search Engines (2023)
  Noah Ziems, Wenhao Yu, Zhihan Zhang, et al.
  13.49
• Evaluating The Robustness Of Retrieval Pipelines With Query Variation Generators (2021)
  Gustavo Penha, Arthur Câmara, Claudia Hauff
  13.36
• Aggretriever: A Simple Approach To Aggregate Textual Representations For Robust Dense Passage Retrieval (2022)
  Sheng-Chieh Lin, Minghan Li, Jimmy Lin
  13.22
• Pseudo-relevance Feedback For Multiple Representation Dense Retrieval (2021)
  Xiao Wang, Craig MacDonald, Nicola Tonellotto, et al.
  12.93
• Improving Passage Retrieval With Zero-shot Question Generation (2022)
  Devendra Singh Sachan, Mike Lewis, Mandar Joshi, et al.
  12.87
• COCO-DR: Combating Distribution Shifts In Zero-shot Dense Retrieval With Contrastive And Distributionally Robust Learning (2022)
  Yue Yu, Chenyan Xiong, Si Sun, et al.
  12.85
• Local Self-attention Over Long Text For Efficient Document Retrieval (2020)
  Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra, et al.
  12.74
• Neural Ranking Models With Multiple Document Fields (2017)
  Hamed Zamani, Bhaskar Mitra, Xia Song, et al.
  12.74
• Hyperbolic Representation Learning For Fast And Efficient Neural Question Answering (2017)
  Yi Tay, Luu Anh Tuan, Siu Cheung Hui
  12.61
• Corpusbrain: Pre-train A Generative Retrieval Model For Knowledge-intensive Language Tasks (2022)
  Jiangui Chen, Ruqing Zhang, Jiafeng Guo, et al.
  12.47
• Do Neural Ranking Models Intensify Gender Bias? (2020)
  Navid Rekabsaz, Markus Schedl
  12.47
• Matryoshka Representation Learning (2022)
  Aditya Kusupati, Gantavya Bhatt, Aniket Rege, et al.
  12.37
• Deepjoin: Joinable Table Discovery With Pre-trained Language Models (2022)
  Yuyang Dong, Chuan Xiao, Takuma Nozawa, et al.
  12.25
• Modeling Diverse Relevance Patterns In Ad-hoc Retrieval (2018)
  Yixing Fan, Jiafeng Guo, Yanyan Lan, et al.
  12.25
• One-shot Labeling For Automatic Relevance Estimation (2023)
  Sean MacAvaney, Luca Soldaini
  12.25
• Improving Query Representations For Dense Retrieval With Pseudo Relevance Feedback (2021)
  Hongchien Yu, Chenyan Xiong, Jamie Callan
  12.10
• Dynamic Trade-off Prediction In Multi-stage Retrieval Systems (2016)
  J. Shane Culpepper, Charles L. A. Clarke, Jimmy Lin
  11.93
• Retrieval-enhanced Machine Learning (2022)
  Hamed Zamani, Fernando Diaz, Mostafa Dehghani, et al.
  11.93
• Learning To Rank In Generative Retrieval (2023)
  Yongqi Li, Nan Yang, Liang Wang, et al.
  11.91
• More Robust Dense Retrieval With Contrastive Dual Learning (2021)
  Yizhi Li, Zhenghao Liu, Chenyan Xiong, et al.
  11.88