Backchannels (e.g., 'yeah', 'mhm', and 'right') are short, non-interruptive feedback signals whose lexical form and prosody jointly convey pragmatic meaning.While prior computational research has largely focused on predicting backchannel timing, the relationship between lexico-prosodic form and meaning remains underexplored.We propose a two-stage framework: first, fine-tuning large language models on dialogue transcripts to derive rich contextual representations; and second, learning a joint embedding space for dialogue contexts and backchannel realizations.We evaluate alignment with human perception via triadic similarity judgments (prosodic and cross-lexical) and a context-backchannel suitability task.Our results demonstrate that the learned projections substantially improve context-backchannel retrieval compared to previous methods.In addition, they reveal that backchannel form is highly sensitive to extended conversational context and that the learned embeddings align more closely with human judgments than raw WavLM features.Pitch length: 89.1 Pitch range: 14.

Vision-language models (VLMs) have shown to be effective at image retrieval based on simple text queries, but text-image retrieval based on conversational input remains a challenge.Consequently, if we want to use VLMs for reference resolution in visually-grounded dialogue, the discourse processing capabilities of these models need to be augmented.To address this issue, we propose fine-tuning a causal large language model (LLM) to generate definite descriptions that summarize coreferential information found in the linguistic context of references.We then use a pretrained VLM to identify referents based on the generated descriptions, zero-shot.We evaluate our approach on a manually annotated dataset of visuallygrounded dialogues and achieve results that, on average, exceed the performance of the baselines we compare against.Furthermore, we find that using referent descriptions based on larger context windows has the potential to yield higher returns.