@inproceedings{leishman-etal-2026-analysing,
    title = "Analysing the role of lexical and temporal information in turn-taking through predictability",
    author = "Leishman, Sean  and
      Wallbridge, Sarenne  and
      Bell, Peter",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.283/",
    pages = "5998--6009",
    ISBN = "979-8-89176-380-7",
    abstract = "Turn-taking is a fundamental component of human communication and is signalled through complex cues distributed across lexical, temporal, and prosodic information. Full-duplex models of spoken dialogue integrate these information sources to produce impressive turn-taking behaviour. Yet, existing evaluations of their turn-taking capabilities do not address which information sources drive predictions.We present a systematic analysis of the role of lexical-temporal features on the predictability of turn structure by examining PairwiseTurnGPT, a full-duplex model of spoken dialogue transcripts. Through PCA, mixed-effects modelling, and temporal surprisal analysis, we reveal context-dependent patterns: linguistic fluency paradoxically creates overconfidence at intermediate completion points, while turn-shift overlap dominates boundary detection. Our findings uncover where lexical-temporal information suffices and where additional cues become necessary, establishing a deeper understanding of how turn-taking cues are distributed and how to evaluate dialogue systems."
}