Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models (2026.acl-long)
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| Challenge: | Backchannels and fillers are important linguistic expressions in dialogue, but often ignored in modern transformer-based language models. |
| Approach: | They use clustering analysis to learn backchannels and fillers in dialogues in English and Japanese and use natural language generation metrics to confirm this. |
| Outcome: | The proposed models can learn representations of backchannels and fillers using three fine-tuning strategies. |
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