A Machine Learning Approach to the Classification of Dialogue Utterances

The purpose of this paper is to present a method for automatic classification of dialogue utterances and the results of applying that method to a corpus. Superficial features of a set of training utterances (which we will call cues) are taken as the basis for finding relevant utterance classes and for extracting rules for assigning these classes to new utterances. Each cue is assumed to partially contribute to the communicative function of an utterance. Instead of relying on subjective judgments for the tasks of finding classes and rules, we opt for using machine learning techniques to guarantee objectivity.Comment: 12 pages, using nemlap.sty, harvard.sty and agsm.bst, to appear in Proceedings of NeMLaP-2, Bilkent University, Ankara, Turke

Towards Understanding Egyptian Arabic Dialogues

Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0308

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

Active Learning for Dialogue Act Classification

Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result