  In this paper, we study transfer learning for the PI and NLI problems, aiming
to propose a general framework, which can effectively and efficiently adapt the
shared knowledge learned from a resource-rich source domain to a resource- poor
target domain. Specifically, since most existing transfer learning methods only
focus on learning a shared feature space across domains while ignoring the
relationship between the source and target domains, we propose to
simultaneously learn shared representations and domain relationships in a
unified framework. Furthermore, we propose an efficient and effective hybrid
model by combining a sentence encoding- based method and a sentence
interaction-based method as our base model. Extensive experiments on both
paraphrase identification and natural language inference demonstrate that our
base model is efficient and has promising performance compared to the competing
models, and our transfer learning method can help to significantly boost the
performance. Further analysis shows that the inter-domain and intra-domain
relationship captured by our model are insightful. Last but not least, we
deploy our transfer learning model for PI into our online chatbot system, which
can bring in significant improvements over our existing system. Finally, we
launch our new system on the chatbot platform Eva in our E-commerce site
AliExpress.
