Liu Yang


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DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling
Jiecao Chen | Liu Yang | Karthik Raman | Michael Bendersky | Jung-Jung Yeh | Yun Zhou | Marc Najork | Danyang Cai | Ehsan Emadzadeh
Findings of the Association for Computational Linguistics: EMNLP 2020

Pre-trained models like BERT ((Devlin et al., 2018) have dominated NLP / IR applications such as single sentence classification, text pair classification, and question answering. However, deploying these models in real systems is highly non-trivial due to their exorbitant computational costs. A common remedy to this is knowledge distillation (Hinton et al., 2015), leading to faster inference. However – as we show here – existing works are not optimized for dealing with pairs (or tuples) of texts. Consequently, they are either not scalable or demonstrate subpar performance. In this work, we propose DiPair — a novel framework for distilling fast and accurate models on text pair tasks. Coupled with an end-to-end training strategy, DiPair is both highly scalable and offers improved quality-speed tradeoffs. Empirical studies conducted on both academic and real-world e-commerce benchmarks demonstrate the efficacy of the proposed approach with speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model.


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Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
Minghui Qiu | Liu Yang | Feng Ji | Wei Zhou | Jun Huang | Haiqing Chen | Bruce Croft | Wei Lin
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist and observed a significant improvement over the existing online model.


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Generating Supplementary Travel Guides from Social Media
Liu Yang | Jing Jiang | Lifu Huang | Minghui Qiu | Lizi Liao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization
Minghui Qiu | Liu Yang | Jing Jiang
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies