TurnTurk-GPT: Text-Based Turn-Taking Prediction for Turkish
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabTurnTurk-GPT is a text-based turn-taking predictor for Turkish, created by fine-tuning a Turkish GPT-2 model with the TurnGPT objective on synthetic and real dialogue. It is evaluated on real Turkish conversation against rule baselines, a zero-shot Turkish language model, and a multilingual end-of-turn detector, using word-level decision points on unpunctuated, lower-cased text with recording-disjoint and speaker-disjoint folds. The model and evaluation protocol are publicly released.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
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TurnTurk-GPT is a text-based turn-taking predictor for Turkish, created by fine-tuning a Turkish GPT-2 model with the TurnGPT objective on synthetic and real dialogue. It is evaluated on real Turkish conversation against rule baselines, a zero-shot Turkish language model, and a multilingual end-of-turn detector, using word-level decision points on unpunctuated, lower-cased text with recording-disjoint and speaker-disjoint folds. The model and evaluation protocol are publicly released.
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- 04What could mislead?
Gains over the zero-shot language model vanish on unseen speakers, and the baselines stay near chance when a speaker pauses.
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Key findings from the stored brief
- TurnTurk-GPT outperforms the multilingual end-of-turn detector under both recording-disjoint and speaker-disjoint evaluation.
- It beats the zero-shot Turkish language model only for speakers seen in training; on unseen speakers they are statistically indistinguishable.
- Removing suffixes of the turn-final word cuts average precision by up to 44%, versus 2% for a sentence-internal word.
- Synthetic dialogue alone yields the weakest trained model.
Gains over the zero-shot language model vanish on unseen speakers, and the baselines stay near chance when a speaker pauses.
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Evidence & sources
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Why this may matter
Turn-taking prediction helps conversational systems decide when to respond. The paper shows that for Turkish, the end-of-turn cue is tied to verb-final morphology, and that generalization to unseen speakers remains a limitation.
Limits and open questions
Gains over the zero-shot language model vanish on unseen speakers, and the baselines stay near chance when a speaker pauses.
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