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TurnTurk-GPT: Text-Based Turn-Taking Prediction for Turkish — arXiv:2610.42010

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.42010
License (as archived): CC0-1.0
Archived: 2026-11-03T07:25:45.681Z
SHA-256 of the archived text: ab85cc9ecb47826f339a9c1b3dd61d2e3815c23c90fa41e1c567d67a5468faa4
Stable URL of this page: https://metraipolis.com/documento/dc_b28ea334a71b

How to cite

TurnTurk-GPT: Text-Based Turn-Taking Prediction for Turkish — arXiv:2610.42010 — arXiv (en), https://arxiv.org/abs/2610.42010 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b28ea334a71b (Metraipolis archive dc_b28ea334a71b, sha256 ab85cc9ecb47826f339a9c1b3dd61d2e3815c23c90fa41e1c567d67a5468faa4)

BibTeX

@misc{dcb28ea334a71b,
  title = {TurnTurk-GPT: Text-Based Turn-Taking Prediction for Turkish — arXiv:2610.42010},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.42010},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b28ea334a71b; sha256 ab85cc9ecb47826f339a9c1b3dd61d2e3815c23c90fa41e1c567d67a5468faa4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.42010. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper presents TurnTurk-GPT, a text-based turn-taking predictor for Turkish obtained by fine-tuning a Turkish GPT-2 model with the TurnGPT objective on synthetic and real dialogue, and evaluates it on real Turkish conversation against rule baselines, a zero-shot Turkish language model and a widely used multilingual end-of-turn detector. Evaluation uses word-level decision points on unpunctuated, lower-cased text approximating the output of streaming speech recognisers, recording-disjoint and speaker-disjoint folds, nested threshold selection and paired cluster bootstrap tests. TurnTurk-GPT outperforms the multilingual detector under both recording-disjoint and speaker-disjoint evaluation. It outperforms the zero-shot language model only when the test speakers occur in training; on unseen speakers the two are statistically indistinguishable. Removing the suffixes of the turn-final word reduces AP by up to 44%, against 2% for a sentence-internal word, locating the Turkish end-of-turn cue in verb-final morphology. Synthetic dialogue alone yields the weakest trained model, and when a speaker pauses the zero-shot and multilingual baselines remain close to chance. The model and the evaluation protocol are publicly released.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.42010 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:20:03.753Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).