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When to generate hedges in peer-tutoring interactions

What it is about

The question is not whether a turn contains a hedge but whether the tutor's next turn should contain one, predicted from the four previous turns. Data are 9,479 turns of face-to-face peer tutoring between teenagers (non-public); about one tutor turn in ten has a hedge. Each past turn is encoded by a sentence embedding, annotated conversational strategies (self-disclosure, praise, teasing, earlier hedges), tutoring strategies (deep or shallow questions, knowledge telling or building), DAMSL dialogue acts from an automatic tagger, rapport ratings, nonverbal behaviour (gaze, smiles, nods) and task context (problem, correctness of the last answer, pre-test scores). Gradient boosting, MLP and LSTM models with class rebalancing all stay low: best F1 0.28 against 0.11 for a dummy baseline. SHAP and an ablation point to the gaze of both partners as the most useful non-text cue.

Ideas

For Mova

32. Politeness and hedging §7 and §11, 38. Dialogue, discourse markers and reported speech §11.3 (the family's dialogue layer); world/src/talk.rs; measure: unit tests per hedging rule. The corpus is private and not usable.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.sigdial-1.53 · arXiv 2307.15582

@inproceedings{abulimiti2023when,
  title = {{When to generate hedges in peer-tutoring interactions}},
  author = {Abulimiti, Alafate and Clavel, Chloé and Cassell, Justine},
  booktitle = {Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue},
  year = {2023},
  doi = {10.18653/v1/2023.sigdial-1.53},
  eprint = {2307.15582},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2023.sigdial-1.53}
}

References

40 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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