@InProceedings{qiu-EtAl:2018:Short,
  author    = {Qiu, Minghui  and  Yang, Liu  and  Ji, Feng  and  Zhou, Wei  and  Huang, Jun  and  Chen, Haiqing  and  Croft, Bruce  and  Lin, Wei},
  title     = {Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {208--213},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/P18-2034}
}