Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
This work improves dialog act prediction for conversational agents, representing an incremental advancement over existing methods.
The paper tackled the problem of predicting dialog acts in conversational agents by introducing a seq2seq model with hierarchical encoding, guided attention, and beam search, achieving 85% accuracy on SwDA and 91.6% on MRDA.
The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.