Sequential Neural Networks for Noetic EndtoEnd Response Selection Abstract   The noetic endtoend response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goaloriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multiturn context. This paper presents our systems that are ranked top 1 on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous stateoftheart models use hierarchybased (utterancelevel and tokenlevel) neural networks to explicitly model the interactions among different turnsxe2x80x99 utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multiturn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multiturn response selection. In addition to ranking top 1 in the challenge, the proposed model outperforms all previous models, including stateoftheart hierarchybased models, on two largescale public multiturn response selection benchmark datasets.

Dense Semantic Matching Network for Multiturn Conversation Mining the semantic information in the text to model the multiturn conversation has attracted great interests. Previous models ignore the capturing and utilizing of the matching patterns at different levels, which causes the loss of the valuable information for the calculation of the matching degree. To address the problem, we propose a dense semantic matching network (DMN). Given a contextresponse pair, DMN first constructs semantic representations for the response candidate and each utterance in the context. Then, DMN models the interaction between the response candidate and each utterance in the context to generate interactive matrices. By processing the interactive matrices with dense convolutional blocks, the hierarchical matching patterns are generated for each utteranceresponse pair. The matching patterns of all the utteranceresponse pairs are finally accumulated in chronological order with bidirectional long shortterm memory network. The final matching score of the response candidate and the multiturn context is finally calculated. We evaluate the performance of our network on the benchmark dataset. The results show that our network yields a significant performance gain compared with other methods.