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.

Context and Knowledge Aware Conversational Model and System Combination for Grounded Response Generation Abstract   Endtoend neuralbased dialogue systems can potentially generate tailored and coherent responses for user inputs. However, most of existing systems produce universal and noninformative responses, and they have not gone beyond chitchat yet. To tackle these problems, 7th Dialog System Technology Challenges (DSTC7Track2) was developed to focus on building a dialogue system that produces informational responses that are grounded on external knowledge. In this study, we propose a Memoryaugmented Hierarchical Recurrent EncoderDecoder, called MHRED, that considers multiturn dialogue context and external knowledge. Furthermore, we apply a combination of multiple dialogue systems. Our final system is an ensemble that combines three modules: a generationbased module, a retrievalbased module, and a reranking module. First, responses are generated by MHRED, and retrieved from a predefined database focusing on informativeness. Next, the reranking module sorts these candidates using several handcrafted features, and finally it selects a response with the highest score. Therefore, this system can return diverse and meaningful responses from various perspectives.  Experimental results show that our proposed MHRED outperforms strong baseline models and that combining multiple dialogue systems significantly improves the automatic evaluation and human evaluations.