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Many natural language processing and information retrieval problems can be formalized as the task of semantic matching.
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
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A Deep Look into Neural Ranking Models for Information Retrieval
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XLNet: Generalized Autoregressive Pretraining for Language Understanding
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Blockwise Self-Attention for Long Document Understanding
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Compressive Transformers for Long-Range Sequence Modelling
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Axial Attention in Multidimensional Transformers
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Overview of the TREC 2019 deep learning track
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Efficient Content-Based Sparse Attention with Routing Transformers
A. Roy, M. T. Saffar, D. Grangier, and A. Vaswani. 2020 · 2003
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Overview of the TREC 2008 Blog Track. In TREC ’08
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The ACL Anthology Network Corpus. In NLPIR4DL ’09 . 54–61
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Learning Deep Structured Semantic Models for Web Search using Clickthrough Data. In CIKM ’13 . 2333–2338
P. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. P. Heck. 2013 · 2013
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Distributed Representations of Words and Phrases and their Compositionality. In NIPS ’13 . 3111–3119
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. 2013 · 2013
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Semantic Matching in Search
H. Li and J. Xu. 2014 · 2014
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A large annotated corpus for learning natural language inference. In EMNLP ’15 . 632–642
Learning to Match Using Local and Distributed Representations of Text for Web Search. In WWW ’17 . 1291–1299
B. Mitra, F. Diaz, and N. Craswell. 2017 · 2017
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Attention is All You Need
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End-to-End Neural Ad-hoc Ranking with Kernel Pooling. In SIGIR ’17 . 55–64
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Deep contextualized word representations
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S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. 2015 · 2015
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun. 2015 · 2015
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The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
R. Lowe, N. Pow, I. Serban, and J. Pineau. 2015 · 2015
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Convolutional Neural Network for Paraphrase Identification. In NAACL ’15 . 901–911
W. Yin and H. Schütze. 2015 · 2015
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A Deep Relevance Matching Model for Ad-hoc Retrieval. In CIKM ’16 . 55–64
J. Guo, Y. Fan, Q. Ai, and W. B. Croft. 2016 · 2016
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Text Matching as Image Recognition. In AAAI ’16 . 2793–2799
L. Pang, Y. Lan, J. Guo, J. Xu, S. Wan, and X. Cheng. 2016 · 2016
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Billion-scale similarity search with GPUs
J. Johnson, M. Douze, and H. Jégou. 2017 · 2017
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Improving Language Understanding by Generative Pre-Training
A. Radford. 2018 · 2018
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WikiQA: A Challenge Dataset for Open-Domain Question Answering. In EMNLP ’15 . 2013–2018
Y. Yang, W. Yih, and C. Meek. 2015 · 2018
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Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce. In WSDM ’18 . 682–690
J. Yu, M. Qiu, J. Jiang, J. Huang, S. Song, W. Chu, and H. Chen. 2018 · 2018
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Deeper Text Understanding for IR with Contextual Neural Language Modeling. In SIGIR ’19
Z. Dai and J. Callan. 2019 · 2019
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Semantic Text Matching for Long-Form Documents. In WWW ’19 . 795–806
J. Jiang, M. Zhang, C. Li, M. Bendersky, N. Golbandi, and M. Najork. 2019 · 2019
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Reformer: The Efficient Transformer
N. Kitaev, L. Kaiser, and A. Levskaya. 2020 · 2020
Closest in time.
Convolutional Neural Network Architectures for Matching Natural Language Sentences. In NIPS ’14 . 2042–2050
B. Hu, Z. Lu, H. Li, and Q. Chen. 2014 · 2050
Closest in time.