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The task of predicting dialog acts (DA) based on conversational dialog is akey component in the development of conversational agents. Accuratelypredicting DAs requires a precise modeling of both the conversation and theglobal tag dependencies. We leverage seq2seq approaches widely adopted inNeural Machine Translation (NMT) to improve the modelling of tag sequentiality.Seq2seq models are known to learn complex global dependencies while currentlyproposed approaches using linear conditional random fields (CRF) only modellocal tag dependencies. In this work, we introduce a seq2seq model tailored forDA classification using: a hierarchical encoder, a novel guided attentionmechanism and beam search applied to both training and inference. Compared tothe state of the art our model does not require handcrafted features and istrained end-to-end. Furthermore, the proposed approach achieves an unmatchedaccuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% onMRDA.

Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue