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learning-to-match

conference paper code des
https://github.com/baidu/AnyQ FAQ-based Question Answering System
ACL 2018 Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms https://github.com/dinghanshen/SWEM
NIPS 2017 Deconvolutional Paragraph Representation Learning https://github.com/dinghanshen/textCNN_public
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
CIKM 2016 A Deep Relevance Matching Model for Ad-hoc Retrieval https://github.com/faneshion/DRMM
https://github.com/faneshion/MatchZoo MatchZoo is a toolkit for text matching. It was developed to facilitate the designing, comparing, and sharing of deep text matching models.
https://github.com/pl8787/MatchPyramid-TensorFlow A simple version of MatchPyramid implement in TensorFlow.
https://github.com/dadashkarimi/Y-Flow Y-Flow is an extension for MatchZoo https://github.com/faneshion/MatchZoo toolkit for text matching.
https://github.com/faneshion/HiNT Implementation of Hierarchical Neural maTching model proposed in SIGIR'18 for ad-hoc retrieval
2014 A Parallel and Efficient Algorithm for Learning to Match
2007 Learning by Doing vs. Learning about Match Quality: Can We Tell Them Apart?
WWW 2018 Matching Resumes to Jobs via Deep Siamese Network
www18-tutorial-deep-matching.pdf Deep Learning for Matching in Search and Recommendation
https://github.com/faneshion/NDRM A repository for Neural Document Ranking Models.
Text Matching as Image Recognition https://github.com/pl8787/MatchPyramid-TensorFlow A simple version of MatchPyramid implement in TensorFlow. Paper https://arxiv.org/abs/1602.06359.
https://github.com/pl8787/textnet-release TextNet: A deep neural network framework for text matching

papers

2013

  • Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality NIPS '13. 3111--3119.
  • Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning Deep Structured Semantic Models for Web Search Using Clickthrough Data CIKM '13. 2333--2338.
  • Fabian L. Wauthier, Michael I. Jordan, and Nebojsa Jojic. 2013. Efficient Ranking from Pairwise Comparisons. In ICML'13. 109--117.
  • Tianqi Chen, Hang Li, Qiang Yang, Yong Yu, General Functional Matrix Factorization Using Gradient Boosting, In Proceedings of 30th International Conference on Machine Learning (ICML), JMLR W&CP 28(1):436-444, 2013.
  • Zhengdong Lu, Hang Li, A Deep Architecture for Matching Short Texts, In Proceedings of Neural Information Processing Systems 26 (NIPS), 1367-1375, 2013.
  • Wei Wu, Hang Li, Jun Xu, Learning Query and Document Similarities from Click-through Bipartite Graph with Metadata, In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM), 687-696, 2013.
  • Wei Wu, Zhengdong Lu, Hang Li, Learning Bilinear Model for Matching Queries and Documents, Journal of Machine Learning Research (JMLR), 14: 2519-2548, 2013.
  • Po-Sen Huang , Xiaodong He , Jianfeng Gao , Li Deng , Alex Acero , Larry Heck, Learning deep structured semantic models for web search using clickthrough data, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, October 27-November 01, 2013, San Francisco, California, USA DSSM T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Proceedings of the 2tth Advances in Neural Information Processing Systems 2013 (NIPS), pages 3111--3119, 2013.

2014

  • Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. Learning semantic representations using convolutional neural networks for web search. In Proceedings of WWW, 2014.
  • Baotian Hu , Zhengdong Lu , Hang Li , Qingcai Chen, Convolutional neural network architectures for matching natural language sentences, Proceedings of the 27th International Conference on Neural Information Processing Systems, p.2042-2050, December 08-13, 2014, Montreal, Canada
  • Yelong Shen , Xiaodong He , Jianfeng Gao , Li Deng , Grégoire Mesnil, Learning semantic representations using convolutional neural networks for web search, Proceedings of the 23rd International Conference on World Wide Web, April 07-11, 2014, Seoul, Korea
  • Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J. Mach. Learn. Res., Vol. 15, 1 (2014), 1929--1958.
  • Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu, A Parallel and Efficient Algorithm for Learning to Match, in Proceedings of IEEE International Conference on Data Mining (ICDM), 971-976, 2014.
  • Hang Li, Jun Xu, Semantic Matching in Search, Foundations and Trends in Information Retrieval, Now Publishers, 2014.
  • Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen, Convolutional Neural Network Architectures for Matching Natural Language Sentences, in Proceedings of Advances in Neural Information Processing Systems 27 (NIPS), 2042-2050, 2014.

2015

  • Mingxuan Wang , Zhengdong Lu , Hang Li , Qun Liu, Syntax-based deep matching of short texts, Proceedings of the 24th International Conference on Artificial Intelligence, p.1354-1361, July 25-31, 2015, Buenos Aires, Argentina
  • Wenpeng Yin and Hinrich Schu ̈tze. 2015a. Convolu- tional neural network for paraphrase identification. In Proceedings of NAACL, pages 901–911.
  • Wenpeng Yin and Hinrich Schu ̈tze. 2015b. Multi- GranCNN: An architecture for general matching of text chunks on multiple levels of granularity. In Pro- ceedings of ACL-IJCNLP, pages 63–73. Multigrancnn
  • Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, and Xueqi Cheng. 2015. A deep architecture for semantic matching with multiple positional sentence representations. arXiv preprint arXiv:1511.08277. MV-LSTM
  • S. Wan, Y. Lan, J. Guo, J. Xu, L. Pang, and X. Cheng. A deep architecture for semantic matching with multiple positional sentence representations. arXiv preprint arXiv:1511.08277, 2015.
  • Mingxuan Wang, Zhengdong Lu, Hang Li, Qun Liu, Syntax-based Deep Matching of Short Texts, in Proceedings of International Conference on Artificial Intelligence (IJCAI’15), 2015.
  • Lin Ma, Zhengdong Lu, Lifeng Shang, Hang Li, Multimodal Convolutional Neural Networks for Matching Image and Sentence. Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV), 2015. to appear.
  • Shuxin Wang, Xin Jiang, Jun Xu, Hang Li, Bin Wang, Incorporating Semantic Knowledge into Latent Matching Model in Search, to appear, 2015.

2016

  • Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016. A Deep Relevance Matching Model for Ad-hoc Retrieval CIKM '16. 55--64.
  • [Yin et al.2016] Wenpeng Yin, Hinrich Schutze, Bing Xi- ¨ang, and Bowen Zhou. 2016. ABCNN: Attentionbased convolutional neural network for modeling sentence pairs. In Transactions of the Association of Computational Linguistics.
  • Liang Pang , Yanyan Lan , Jiafeng Guo , Jun Xu , Shengxian Wan , Xueqi Cheng, Text matching as image recognition, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona MatchPyramid
  • S. Wan, Y. Lan, J. Xu, J. Guo, L. Pang, and X. Cheng. Match-srnn: Modeling the recursive matching structure with spatial rnn. In IJCAI, 2016. Match-SRNN
  • [Wang and Jiang, 2016] Shuohang Wang and Jing Jiang. A compare-aggregate model for matching text sequences. arXiv preprint arXiv:1611.01747, 2016.
  • Liu Yang , Qingyao Ai , Jiafeng Guo , W. Bruce Croft, aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, October 24-28, 2016, Indianapolis, Indiana, USA
  • Liang Pang , Yanyan Lan , Jiafeng Guo , Jun Xu , Shengxian Wan , Xueqi Cheng, Text matching as image recognition, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona
  • Jiafeng Guo , Yixing Fan , Qingyao Ai , W. Bruce Croft, A Deep Relevance Matching Model for Ad-hoc Retrieval, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, October 24-28, 2016, Indianapolis, Indiana, USA
    • DRMM
  • Yuan Tang. 2016. TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning. arXiv preprint arXiv:1612.04251 (2016).
  • Liu Yang , Qingyao Ai , Jiafeng Guo , W. Bruce Croft, aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, October 24-28, 2016, Indianapolis, Indiana, USA

2017

  • Zhiguo Wang, Wael Hamza, Radu Florian. Bilateral Multi-Perspective Matching for Natural Language Sentences. 2017.
  • Kai Hui, Andrew Yates, Klaus Berberich, and Gerard de Melo. 2017. PACRR: A Position-Aware Deep Model for Relevance Matching in Information Retrieval. In EMNLP 2017.
  • Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, and Xueqi Cheng. 2017. DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval. In CIKM 2017.
    • DeepRank
  • Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match Using Local and Distributed Representations of Text for Web Search. In WWW 2017.
  • Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, and W. Bruce Croft. 2017. Neural ranking models with weak supervision. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017). ACM, pages 65–74.
  • C. Xiong, Z. Dai, J. Callan, Z. Liu, and R. Power. End-to-end neural ad-hoc ranking with kernel pooling. In Proceedings of the 40th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM. 2017.
  • Bhaskar Mitra , Fernando Diaz , Nick Craswell, Learning to Match using Local and Distributed Representations of Text for Web Search, Proceedings of the 26th International Conference on World Wide Web, April 03-07, 2017, Perth, Australia
  • Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match Using Local and Distributed Representations of Text for Web Search WWW '17. 1291--1299.
    • DUET

2018

  • Zhuyun Dai, Chenyan Xiong, Jamie Callan, and Zhiyuan Liu. 2018. Convolutional neural networks for soft-matching n-grams in ad-hoc search. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM 2018). ACM, pages 126–134.
  • Jinfeng Rao, Wei Yang, Yuhao Zhang, Ferhan Ture, and Jimmy Lin. 2018. MultiPerspective Relevance Matching with Hierarchical ConvNets for Social Media Search. (2018). arXiv:arXiv:1805.08159 Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
  • Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information

Deconvolutional Latent-Variable Model for Text Sequence Matching Towards Be�er Text Understanding and Retrieval through Kernel Entity Salience Modeling Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval

survey

PPT

  • Learning to Match, Hang Li, PPT

Learning to Match noahlab Learning to Match Ontologies on the Semantic Web Learning to Match, Hang Li Learning to Match, Themis Mavridis Learning to Match

Semantic Question Matching with Deep Learning https://engineering.quora.com/Semantic-Question-Matching-with-Deep-Learning

Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction

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