Response Selection of Interview-Based Dialog System Using User Focus and Semantic Orientation

  • Shunsuke TadaEmail author
  • Yuya Chiba
  • Takashi Nose
  • Akinori Ito
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 82)


This research examined the response selection method of an interview-based dialog system that obtains the user’s information by the chat-like conversation. In the interview dialog, the system should ask about the subject that the user is interested in to obtain the user’s information efficiently. In this paper, we proposed the method to select the system’s utterance based on the user’s emotion to a focus detected from the user’s utterance. We prepared the question types corresponding to the semantic orientation, such as the positive, neutral, and negative. The focus was detected by the CRF, and the question type was estimated from the user’s utterance and the system’s previous utterance.


Spoken dialog system User focus Interview dialog Conditional random field Support vector machine 


  1. 1.
    Bickmore, T.W., Picard, R.W.: Establishing and maintaining long-term human-computer relationships. ACM Trans. Comput.-Human Interact. 12(2), 293–327 (2005)Google Scholar
  2. 2.
    Yu, Z., Nicolich-Henkin, L., Black, A., Rudnicky, A.I.: A wizard-of-Oz study on a non-task-oriented dialog systems that reacts to user engagement. In: Proceedings of SIGDIAL, pp. 55–63 (2016)Google Scholar
  3. 3.
    Pargellis, A.N., Kuo, H.J., Lee, C.: An automatic dialogue generation platform for personalized dialogue applications. Speech Commun. 42(3–4), 329–351 (2004)CrossRefGoogle Scholar
  4. 4.
    Stent, A., Stenchikova, S., Marge, M.: Dialog systems for surveys: the rate-a-course system. In: Proceedings of IEEE/ACL Spoken Language Technology Workshop, pp. 210–213 (2006)Google Scholar
  5. 5.
    Johnston, M., Ehlen, P., Conrad, F.G., Schober, M.F., Antoun, C., Fail, S., Hupp, A., Vickers, L., Yan, H., Zhang, C.: Spoken dialog systems for automated survey interviewing. In: Proceedings of SIGDIAL, pp. 329–333 (2013)Google Scholar
  6. 6.
    Yoshino, K., Kawahara, T.: Information navigation system based on POMDP that tracks user focus. In: Proceedings of SIGDIAL, pp. 32–40 (2014)Google Scholar
  7. 7.
    Chiba, Y., Ito, A.: Estimation of user’s willingness to talk about the topic: analysis of interviews between humans. In: Dialogues with Social Robots, pp. 411–419 (2016)Google Scholar
  8. 8.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)Google Scholar
  9. 9.
    Maekawa, K., Koiso, H., Furui, S., Isahara, H.: Spontaneous speech corpus of Japanese. In: Proceedings of LREC, pp. 947–952 (2000)Google Scholar
  10. 10.
    Neologism dictionary based on the language resources on the web for MeCab,

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Shunsuke Tada
    • 1
    Email author
  • Yuya Chiba
    • 1
  • Takashi Nose
    • 1
  • Akinori Ito
    • 1
  1. 1.Graduate School of EngineeringTohoku UniversitySendaiJapan

Personalised recommendations