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An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue

Summary

Most dialogue act classifiers ignore the user's emotional state. This paper asks whether facial cues of confusion can help label student moves in textual, task-oriented tutoring dialogue. The data come from a human-human tutoring study in introductory computer programming. Students typed to tutors while webcams recorded their faces, and the tutors never saw this footage. The 14 best-quality videos were manually coded with Ekman's FACS for Action Unit 4 (AU4), a brow-lowering movement that prior work links to confusion. AU4 presence was encoded over several time windows, both before the target utterance and across earlier turns. These features were incorporated into dialogue act classifiers whose vector-based framework closely resembles the one Sridhar et al. (2009) used for their maximum-entropy modeling. Separate classifiers were trained per act, with feature selection. The affect-enriched models were significantly better at recognizing GROUNDING and REQUEST FOR FEEDBACK acts. The work is an early demonstration that a nonverbal affective channel can improve dialogue act recognition. This matters because webcams are now widespread.

Key points

  • Corpus: 48 tutoring dialogues in total, of which 14 high-quality videos were used. These run 11 h 55 min and cover 3 female and 11 male students working with 2 tutors.
  • Annotation reliability: Dialogue act annotation reached κ=0.80. Task annotation scored a simple κ of .56 and a weighted κ of .80. GROUNDING was the most frequent student act, at about 21%.
  • Features: AU4 presence was measured in 1-, 5- and 10-second windows, using a dialogue history of up to three utterances back.
  • Grounding: When AU4 was absent just before an utterance, a grounding act such as "Ok" was more likely.
  • Requests for feedback: These were predicted by AU4 three turns earlier and again just before the message, suggesting a delayed and recurring display of confusion.
  • Limitations: Gains for QUESTION, POSITIVE FEEDBACK and EXTRA-DOMAIN acts were not statistically significant. The sample was small because manual tagging is costly. The manual coding likely represents an upper bound for what automatic facial analysis could achieve.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Speech act theory

Cited by

8 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 5 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{boyer2011affect,
  title = {{An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue}},
  author = {Boyer, Kristy and Grafsgaard, Joseph and Ha, Eun Young and Phillips, Robert and Lester, James},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1119/}
}

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