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Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs) achieved significant advances and have become a key component for modern open-domain question-answering systems.
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Supervised transfer learning for product information question answering
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Multi-level abstraction convolutional model with weak supervision for information retrieval
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Teaching machines to ask questions
Kaichun Yao, Libo Zhang, Tiejian Luo, Lili Tao, and Yanjun Wu. 2018 · 2018
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Synthetic qa corpora generation with roundtrip consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019 · 2019
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Deeper text understanding for ir with contextual neural language modeling
Zhuyun Dai and Jamie Callan. 2019 · 2019
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Multi-step retriever-reader interaction for scalable open-domain question answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Unified language model pre-training for natural language understanding and generation
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Eli5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019
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Parameter-efficient transfer learning for nlp
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
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Cross-lingual training for automatic question generation
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Content-based weak supervision for ad-hoc re-ranking
Sean MacAvaney, Andrew Yates, Kai Hui, and Ophir Frieder. 2019 · 2019
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Domain adaptive dialog generation via meta learning
Kun Qian and Zhou Yu. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Select and attend: Towards controllable content selection in text generation
Xiaoyu Shen, Jun Suzuki, Kentaro Inui, Hui Su, Dietrich Klakow, and Satoshi Sekine. 2019a · 2019
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Improving latent alignment in text summarization by generalizing the pointer generator
Xiaoyu Shen, Yang Zhao, Hui Su, and Dietrich Klakow. 2019b · 2019
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Domain adaptation for enterprise email search
Brandon Tran, Maryam Karimzadehgan, Rama Kumar Pasumarthi, Michael Bendersky, and Donald Metzler. 2019 · 2019
Unsupervised neural machine translation for low-resource domains via meta-learning
Cheonbok Park, Yunwon Tae, TaeHee Kim, Soyoung Yang, Mohammad Azam Khan, Lucy Park, and Jaegul Choo. 2021 · 2021
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Cross-lingual learning for text processing: A survey
Matúš Pikuliak, Marián Šimko, and Mária Bieliková. 2021 · 2021
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Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021 · 2021
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Few-shot question answering by pretraining span selection
Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, and Omer Levy. 2021 · 2021
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Towards robust neural retrieval models with synthetic pre-training
Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, and Avirup Sil. 2021 · 2021
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BERT has a mouth, and it must speak: BERT as a Markov random field language model
Alex Wang and Kyunghyun Cho. 2019 · 2019
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Adversarial domain adaptation for machine reading comprehension
Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, and Hongning Wang. 2019 · 2019
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Addressing semantic drift in question generation for semi-supervised question answering
Shiyue Zhang and Mohit Bansal. 2019 · 2019
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Coarse-grain fine-grain coattention network for multi-evidence question answering
Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, and Richard Socher. 2019 · 2019
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Pre-training tasks for embedding-based large-scale retrieval
Wei-Cheng Chang, X Yu Felix, Yin-Wen Chang, Yiming Yang, and Sanjiv Kumar. 2020 · 2020
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Esam: Discriminative domain adaptation with non-displayed items to improve long-tail performance
Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo Deng. 2020 · 2020
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Qa dataset explosion: A taxonomy of nlp resources for question answering and reading comprehension
Anna Rogers, Matt Gardner, and Isabelle Augenstein. 2021 · 2021
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Question answering for the curated web: Tasks and methods in qa over knowledge bases and text collections
Rishiraj Saha Roy and Avishek Anand. 2021 · 2021
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End-to-end training of neural retrievers for open-domain question answering
Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L Hamilton, and Bryan Catanzaro. 2021 · 2021
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Can question generation debias question answering models? a case study on question–context lexical overlap
Kazutoshi Shinoda, Saku Sugawara, and Akiko Aizawa. 2021 · 2021
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Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021
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End-to-end training of multi-document reader and retriever for open-domain question answering
Devendra Singh, Siva Reddy, Will Hamilton, Chris Dyer, and Dani Yogatama. 2021 · 2021
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Few-shot text ranking with meta adapted synthetic weak supervision
Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, and Paul Bennett. 2021 · 2021
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Beir: A heterogeneous benchmark for zero-shot evaluation of information retrieval models
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021 · 2021
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Adaptable and interpretable neural memoryover symbolic knowledge
Pat Verga, Haitian Sun, Livio Baldini Soares, and William Cohen. 2021 · 2021
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Tsdae: Using transformer-based sequential denoising auto-encoderfor unsupervised sentence embedding learning
Kexin Wang, Nils Reimers, and Iryna Gurevych. 2021a · 2021
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Bert-based dense retrievers require interpolation with bm25 for effective passage retrieval
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Consert: A contrastive framework for self-supervised sentence representation transfer
Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, and Weiran Xu. 2021 · 2021
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Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach
Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang. 2021b · 2021
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A review on question generation from natural language text
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Distantly-supervised dense retrieval enables open-domain question answering without evidence annotation
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