GLSE: GlobalLocal Selective Encoding for Response Generation in Neural Conversation Model How to generate relevant and informative response is one of the core topics in response generation area. Following the task formulation of neural machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. However, the dialogue model tends to generate safe, commonplace responses (e.g., I donu0027t know) regardless of the input, when learning to maximize the likelihood of response for the given message in an almost lossless manner just like MT. Different from existing works, we propose a GlobalLocal Selective Encoding model (GLSE) to extend the seq2seq framework to generate more relevant and informative responses. Specifically, two types of selective gate network are introduced in this work: (i) A local selective wordsentence gate is added after encoding phase of Seq2Seq learning framework, which learns to tailor the original message information and generates a selected input representation. (ii) A global selective bidirectionalcontext gate is set to control the bidirectional information flow from a BiGRU based encoder to decoder. Empirical studies indicate the advantage of our model over several classical and strong baselines.

Generating Diverse Conversation Responses by Creating and Ranking Multiple Candidates Abstract   This paper introduces our systems built for Track 2 of Dialog System Technology Challenge 7 (DSTC7). This challenge track aimed to evaluate the response generation methods using fully datadriven conversation models in a knowledgegrounded setting, where textual facts were provided as the knowledge for each contextresponse pair. The sequencetosequence models have achieved impressive results in machine translation and have also been widely used for endtoend generative conversation modelling. However, they tended to output dull and repeated responses in previous studies. Our work aims to promote the diversity of endtoend conversation response generation by adopting a twostage pipeline. 1) Create multiple responses for an input context together with its textual facts. At this stage, two different models are designed, i.e., a variational generative (VariGen) model and a retrievalbased (Retrieval) model. 2) Rank and return the most relevant response by training a topic coherence discrimination (TCD) model for calculating ranking scores. In our experiments, we demonstrated the effectiveness of the response ranking strategy and the external textual knowledge for generating better responses. According to the official evaluation results, our Retrieval and VariGen systems ranked first and second respectively among all participant systems on Entropy metrics which measured the objective diversity of generated responses. Besides, the VariGen system ranked second on NIST and METEOR metrics which measured the objective quality of generated responses.