  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
(https://consumerservice.taobao.com/online-help) and observed a significant
improvement over the existing online model.
