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Sentiment Classification towards Question Answering with Hierarchical Matching Network

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Academic year: 2020

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Figure

Figure 2: The overview of our approach to QA-style sentiment classification where SQi denotes the i-th sentencein question text, SAj denotes the j-th sentence in answer text, HQi and HAj denote the contextual representationsfor SQi and SAj respectively, V[i,j] denotes the bidirectional matching vector for [SQi, SAj] unit through QAbidirectional matching layer, and R is the QA text pair representation refined by self-matching attention layer.
Figure 3: The detail architecture of QA bidirectionalmatching mechanism.
Table 2: Performance of our approaches to QA-style sentiment classification in all domains.
Table 3: The proposed approach vs. several strong baseline approaches in all domains.

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