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The Conversational Discourse Unit: Identification and Its Role in Conversational Turn-taking Management

Paper · 2023 · Junfei Hu and Liesbeth Degand

Summary

Spoken conversation is usually segmented along one dimension at a time: syntax, prosody or pragmatics. Turn-taking research also disagrees about which of these cues best governs when the next speaker starts. Hu and Degand propose the conversational discourse unit (CDU). A CDU stays open until three boundaries line up: its syntactic one (a dependency clause), its prosodic one (an intonation unit) and its pragmatic one (a communicative action). The three layers are therefore mapped onto each other instead of being analysed in isolation. The authors apply this scheme to natural conversation and obtain five CDU configurations. They then ask two questions: how strongly each boundary kind predicts a change of speaker, and how quickly the next turn follows each CDU type. All three boundary kinds are significantly tied to turn-taking. Pragmatic boundaries show the strongest link, prosodic boundaries a weaker one and syntactic boundaries the weakest. Transitions are fastest after the pragmatic-syntax-bound CDU. The authors conclude that recognising meaning and action, and how early a unit's gist becomes available, matters most for the timing of turns.

Key points

  • A CDU closes only when its syntactic, prosodic and pragmatic boundaries all coincide. The model extends Degand & Simon (2009), which combined only syntax and prosody.
  • Five CDU types are distinguished, including a congruent CDU and a pragmatic-syntax-bound CDU. Post-predicate duration for the congruent type was measured on 80 cases sampled at random from 130.
  • Strength of association with turn-taking runs pragmatic > prosodic > syntactic. Transition speed differs by CDU type, and the pragmatic-syntax-bound CDU is the fastest.
  • The motivating background is that turn gaps across 10 languages average roughly 7–469 ms (Stivers et al., 2009). This is far below the 600–1,200 ms needed to produce a single word, so listeners must project where a turn will end.
  • The authors acknowledge a limitation: pragmatic units were identified mostly through semantic understanding of the utterance. Surface rules for pragmatic boundaries are still needed before the method can scale to larger data.

Interesting ideas

  • I1. Treat a discourse unit as open while any one of several parallel layers (syntax, prosody, action) is still incomplete, so a segment boundary means agreement among layers rather than a single cue.
  • I2. Classify units by the way their layer boundaries line up (congruent, pragmatic-syntax-bound, and so on) instead of using one flat unit, so the mismatch pattern becomes a feature in its own right.
  • I3. Evaluate each candidate boundary by counting how often it is actually followed by the target event and how often it is not, then compare cue types by strength of association.
  • I4. Use the amount of material after the head predicate as a rough proxy for how early a unit's gist is available to the listener.
  • I5. Languages front-load the same gist differently, for example English fronts wh-words while Mandarin does not, so an early act cue in one language may surface late, or through other means, in another.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (frames): prag could close a speech-act segment only when the UD clause is complete and the act is complete, giving explainable segments instead of cutting at punctuation.
  • I2 (tree): the shape of the mismatch between a UD clause and the act/sentence span can be logged as a typed feature, which helps diagnose run-on or fragment parses in dialogue text.
  • I3 (learn): the same followed/not-followed tally gives a cheap negative-control check for whether a segmentation cue in silver data predicts anything beyond chance.
  • I4 (back): when generating answers, generation could prefer orders that place the predicate and question word early, so the reader grasps the gist sooner.
  • I5 (frames): speech-act detection should look for action cues such as wh-words or negation wherever they occur, without assuming one fixed position, especially for text routed through translation.

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

 Institutions

🎓UCLouvainLouvain-la-Neuve · Belgium20 works · 3,741 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPhilosophy · 284 works

🧭 Topics 2Research subjects and languages the work is about.Dialogue systems · 624 worksDiscourse analysis · 466 works

Cite

Cite as Junfei Hu and Liesbeth Degand. 2023. The Conversational Discourse Unit: Identification and Its Role in Conversational Turn-taking Management. Dialogue & Discourse.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.5210/dad.2023.203

@inproceedings{hu2023conversational,
  title = {{The Conversational Discourse Unit: Identification and Its Role in Conversational Turn-taking Management}},
  author = {Junfei Hu and Liesbeth Degand},
  booktitle = {Dialogue \& Discourse},
  year = {2023},
  doi = {10.5210/dad.2023.203},
  url = {https://doi.org/10.5210/dad.2023.203}
}

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