Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models
This work addresses the problem of making language models more conversational and human-like for dialogue systems, though it is incremental as it builds on existing fine-tuning methods.
The study tackled the under-representation of backchannels and fillers in transformer-based language models by fine-tuning them on annotated dialogue corpora, resulting in increased silhouette scores and improved natural language generation metrics that better resemble human utterances.
Backchannels and fillers are important linguistic expressions in dialogue, but are under-represented in modern transformer-based language models (LMs). Our work studies the representation of them in language models using three fine-tuning strategies. The models are trained on three dialogue corpora in English and Japanese, where backchannels and fillers are preserved and annotated, to investigate how fine-tuning can help LMs learn their representations. We first apply clustering analysis to the learnt representation of backchannels and fillers, and have found increased silhouette scores in representations from fine-tuned models, which suggests that fine-tuning enables LMs to distinguish the nuanced semantic variation in different backchannel and filler use. We also use natural language generation (NLG) metrics to confirm that the utterances generated by fine-tuned language models resemble human-produced utterances more closely. Our findings suggest the potentials of transforming general LMs into conversational LMs that are more capable of producing human-like languages adequately.