Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
arXiv:2002.08801
Abstract
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.
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- Neural Machine Translation by Jointly Learning to Align and Translate
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
- Effective Approaches to Attention-based Neural Machine Translation
- A Persona-Based Neural Conversation Model