Guiding attention in Sequence-to-sequence models for Dialogue Act prediction

Understand

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.