QuantumInspired DMATTBiGRU for Conversational Sentiment Analysis Conversational sentiment analysis (CSA) is emergent research field in natural language processing (NLP). This brings a lot of new issues worth studying and directions worth exploring. At the same time, there are many difficulties that need to be overcome. There are lot of challenges, such as lacking of effective deep learning models and being short of appropriate datasets. Inspired by the concept of density matrix in quantum mechanics, we propose a novel attention mechanism called DMATT and apply it to conversational sentiment analysis tasks. In the experiment, we find that deep learning model combined with DMATT has a great improvement in test results compared to the model with traditional attention mechanism. Recurrent neural networks (RNN) and their variants LSTM and GRU are very effective choices in solving time series problem such as conversational sentiment analysis tasks. In this paper, we propose a new model combining GRU and DMATT called DMATTBiGRU. We experiment in multiple datasets, one of which is called ScenarioSA collected by ourselves.

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.