3 research outputs found
Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training
Dialogue Act (DA) classification is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DA classification problem ranging from multi-classification to
structured prediction, which suffer from two limitations: a) these methods are
either handcrafted feature-based or have limited memories. b) adversarial
examples can't be correctly classified by traditional training methods. To
address these issues, in this paper we first cast the problem into a question
and answering problem and proposed an improved dynamic memory networks with
hierarchical pyramidal utterance encoder. Moreover, we apply adversarial
training to train our proposed model. We evaluate our model on two public
datasets, i.e., Switchboard dialogue act corpus and the MapTask corpus.
Extensive experiments show that our proposed model is not only robust, but also
achieves better performance when compared with some state-of-the-art baselines
Dialogue Act Classification with Context-Aware Self-Attention
Recent work in Dialogue Act classification has treated the task as a sequence
labeling problem using hierarchical deep neural networks. We build on this
prior work by leveraging the effectiveness of a context-aware self-attention
mechanism coupled with a hierarchical recurrent neural network. We conduct
extensive evaluations on standard Dialogue Act classification datasets and show
significant improvement over state-of-the-art results on the Switchboard
Dialogue Act (SwDA) Corpus. We also investigate the impact of different
utterance-level representation learning methods and show that our method is
effective at capturing utterance-level semantic text representations while
maintaining high accuracy.Comment: NAACL-HLT 2019. 7 pages, 3 figure
Effective Incorporation of Speaker Information in Utterance Encoding in Dialog
In dialog studies, we often encode a dialog using a hierarchical encoder
where each utterance is converted into an utterance vector, and then a sequence
of utterance vectors is converted into a dialog vector. Since knowing who
produced which utterance is essential to understanding a dialog, conventional
methods tried integrating speaker labels into utterance vectors. We found the
method problematic in some cases where speaker annotations are inconsistent
among different dialogs. A relative speaker modeling method is proposed to
address the problem. Experimental evaluations on dialog act recognition and
response generation show that the proposed method yields superior and more
consistent performances.Comment: 8+1 pages, 3 figures, and 5 tables. Rejected by SIGDIAL 201