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A Study on Dialog Act Recognition Using Character-Level Tokenization

  • Eugénio Ribeiro
  • Ricardo Ribeiro
  • David Martins de Matos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11089)

Abstract

Dialog act recognition is an important step for dialog systems since it reveals the intention behind the uttered words. Most approaches on the task use word-level tokenization. In contrast, this paper explores the use of character-level tokenization. This is relevant since there is information at the sub-word level that is related to the function of the words and, thus, their intention. We also explore the use of different context windows around each token, which are able to capture important elements, such as affixes. Furthermore, we assess the importance of punctuation and capitalization. We performed experiments on both the Switchboard Dialog Act Corpus and the DIHANA Corpus. In both cases, the experiments not only show that character-level tokenization leads to better performance than the typical word-level approaches, but also that both approaches are able to capture complementary information. Thus, the best results are achieved by combining tokenization at both levels.

Keywords

Dialog act recognition Character-level Switchboard dialog act corpus DIHANA corpus Multilinguality 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Eugénio Ribeiro
    • 1
    • 2
  • Ricardo Ribeiro
    • 1
    • 3
  • David Martins de Matos
    • 1
    • 2
  1. 1.L²F – Spoken Language Systems LaboratoryINESC-IDLisboaPortugal
  2. 2.Instituto Superior TécnicoUniversidade de LisboaLisboaPortugal
  3. 3.Instituto Universitário de Lisboa (ISCTE-IUL)LisboaPortugal

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