Skip navigation
User training | Reference and search service

Library catalog

Retrievo
EDS
b-on
More
resources
Content aggregators
Please use this identifier to cite or link to this item:

acessibilidade

http://hdl.handle.net/10071/20151
acessibilidade
Title: Deep dialog act recognition using multiple token, segment, and context information representations
Authors: E. Ribeiro
Ribeiro, R.
De Matos, D.
Issue Date: 2019
Publisher: AI Access Foundation
Abstract: Automatic dialog act recognition is a task that has been widely explored over the years. In recent works, most approaches to the task explored different deep neural network architectures to combine the representations of the words in a segment and generate a segment representation that provides cues for intention. In this study, we explore means to generate more informative segment representations, not only by exploring different network architectures, but also by considering different token representations, not only at the word level, but also at the character and functional levels. At the word level, in addition to the commonly used uncontextualized embeddings, we explore the use of contextualized representations, which are able to provide information concerning word sense and segment structure. Character-level tokenization is important to capture intention-related morphological aspects that cannot be captured at the word level. Finally, the functional level provides an abstraction from words, which shifts the focus to the structure of the segment. Additionally, we explore approaches to enrich the segment representation with context information from the history of the dialog, both in terms of the classifications of the surrounding segments and the turn-taking history. This kind of information has already been proved important for the disambiguation of dialog acts in previous studies. Nevertheless, we are able to capture additional information by considering a summary of the dialog history and a wider turn-taking context. By combining the best approaches at each step, we achieve performance results that surpass the previous state-of-the-art on generic dialog act recognition on both the Switchboard Dialog Act Corpus (SwDA) and the ICSI Meeting Recorder Dialog Act Corpus (MRDA), which are two of the most widely explored corpora for the task. Furthermore, by considering both past and future context, similarly to what happens in an annotation scenario, our approach achieves a performance similar to that of a human annotator on SwDA and surpasses it on MRDA.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/20151
DOI: 10.1613/jair.1.11594
ISSN: 1076-9757
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-64052
Accession number: WOS:000507355600013
Appears in Collections:IT-RI - Artigo em revista internacional com arbitragem científica

Files in This Item:
acessibilidade
File Description SizeFormat 
11594-Article (PDF)-22536-1-10-20191130.pdfVersão Editora547.83 kBAdobe PDFView/Open


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Currículo DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.