End-to-end multi-level dialog act recognition

The three-level dialog act annotation scheme of the DIHANA corpus poses a multi-level classification problem in which the bottom levels allow multiple or no labels for a single segment. We approach automatic dialog act recognition on the three levels using an end-to-end approach, in order to implicitly capture relations between them. Our deep neural network classifier uses a combination of word- and character-based segment representation approaches, together with a summary of the dialog history and information concerning speaker changes. We show that it is important to specialize the generic segment representation in order to capture the most relevant information for each level. On the other hand, the summary of the dialog history should combine information from the three levels to capture dependencies between them. Furthermore, the labels generated for each level help in the prediction of those of the lower levels. Overall, we achieve results which surpass those of our previous approach using the hierarchical combination of three independent per-level classifiers. Furthermore, the results even surpass the results achieved on the simplified version of the problem approached by previous studies, which neglected the multi-label nature of the bottom levels and only considered the label combinations present in the corpus.info:eu-repo/semantics/publishedVersio

Dialogue Act Recognition via CRF-Attentive Structured Network

Dialogue Act Recognition (DAR) 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 DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.Comment: 10 pages, 4figure

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class