Aligning Backchannel and Dialogue Context Representations via Contrastive LLM Fine-Tuning

• URL: http://arxiv.org/abs/2604.16622v1
• Date: Fri, 17 Apr 2026 18:19:32 GMT
• Title: Aligning Backchannel and Dialogue Context Representations via Contrastive LLM Fine-Tuning
• Abstract summary: Backchannels are short, non-interruptive feedback signals whose lexical form and prosody jointly convey pragmatic meaning.<n>We propose a two-stage framework: first, fine-tuning large language models on dialogue transcripts to derive rich contextual representations.<n>We evaluate alignment with human perception via triadic similarity judgments (prosodic and cross-lexical) and a context-backchannel suitability task.
• Score: 8.889205646367058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness [45.06366615980232]
  We introduce SDiaReward, an end-to-end multi-turn reward model trained on SDiaReward-Dataset.<n>It operates directly on full multi-turn speech episodes and is optimized with pairwise preference supervision.<n>Experiments demonstrate that SDiaReward achieves state-of-the-art pairwise preference accuracy.
  arXiv  Detail & Related papers  (2026-03-16T06:39:30Z)
• Implicit Counterfactual Learning for Audio-Visual Segmentation [50.69377287012591]
  We propose the implicit counterfactual framework (ICF) to achieve unbiased cross-modal understanding.<n>Due to the lack of semantics, heterogeneous representations may lead to erroneous matches.<n>We introduce the multi-granularity implicit text (MIT) involving video-, segment- and frame-level as the bridge to establish the modality-shared space.
  arXiv  Detail & Related papers  (2025-07-28T11:46:35Z)
• Yeah, Un, Oh: Continuous and Real-time Backchannel Prediction with Fine-tuning of Voice Activity Projection [24.71649541757314]
  Short backchannel utterances such as "yeah" and "oh" play a crucial role in facilitating smooth and engaging dialogue.<n>This paper proposes a novel method for real-time, continuous backchannel prediction using a fine-tuned Voice Activity Projection model.
  arXiv  Detail & Related papers  (2024-10-21T11:57:56Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling [80.51094098799736]
  We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder. BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
  arXiv  Detail & Related papers  (2022-04-18T03:51:46Z)
• DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling [3.3578533367912025]
  We propose a framework that incorporates augmented versions of a dialogue context into the learning objective. We show that our proposed augmentation method outperforms previous data augmentation approaches.
  arXiv  Detail & Related papers  (2022-04-15T23:39:41Z)
• DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings [33.89889949577356]
  We propose DialogueCSE, a dialogue-based contrastive learning approach to tackle this issue. We evaluate our model on three multi-turn dialogue datasets: the Microsoft Dialogue Corpus, the Jing Dong Dialogue Corpus, and the E-commerce Dialogue Corpus.
  arXiv  Detail & Related papers  (2021-09-26T13:25:41Z)
• $C^3$: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues [97.25466640240619]
  Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. We propose a novel approach of Compositional Counterfactual Contrastive Learning to develop contrastive training between factual and counterfactual samples in video-grounded dialogues.
  arXiv  Detail & Related papers  (2021-06-16T16:05:27Z)
• Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues [73.04906599884868]
  We propose a novel framework of Reasoning Paths in Dialogue Context (PDC) PDC model discovers information flows among dialogue turns through a semantic graph constructed based on lexical components in each question and answer. Our model sequentially processes both visual and textual information through this reasoning path and the propagated features are used to generate the answer.
  arXiv  Detail & Related papers  (2021-03-01T07:39:26Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.