The paper proposes CHIM, a chunk-wise importance matrix for representing user and product attributes, and reports that injecting attributes into attention is generally the worst of four locations in a BiLSTM sentiment classifier.
Neural Personalized Response Generation as Domain Adaptation
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abstract
In this paper, we focus on the personalized response generation for conversational systems. Based on the sequence to sequence learning, especially the encoder-decoder framework, we propose a two-phase approach, namely initialization then adaptation, to model the responding style of human and then generate personalized responses. For evaluation, we propose a novel human aided method to evaluate the performance of the personalized response generation models by online real-time conversation and offline human judgement. Moreover, the lexical divergence of the responses generated by the 5 personalized models indicates that the proposed two-phase approach achieves good results on modeling the responding style of human and generating personalized responses for the conversational systems.
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cs.CL 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Rethinking Attribute Representation and Injection for Sentiment Classification
The paper proposes CHIM, a chunk-wise importance matrix for representing user and product attributes, and reports that injecting attributes into attention is generally the worst of four locations in a BiLSTM sentiment classifier.