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Found 7 papers, 3 papers with code
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems
Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge.
BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning
Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation.
An Emotion-based Korean Multimodal Empathetic Dialogue System
We propose a Korean multimodal dialogue system targeting emotion-based empathetic dialogues because most research in this field has been conducted in a few languages such as English and Japanese and in certain circumstances.
Enhancing Dialogue Speech Recognition with Robust Contextual Awareness via Noise Representation Learning
To maximize the advantage of context awareness, our approach includes decoder pre-training using text-based dialogue data and noise representation learning for a context encoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO
Iterative self-improvement, a concept extending beyond personal growth, has found powerful applications in machine learning, particularly in transforming weak models into strong ones.
Adversarial DPO: Harnessing Harmful Data for Reducing Toxicity with Minimal Impact on Coherence and Evasiveness in Dialogue Agents
Recent advancements in open-domain dialogue systems have been propelled by the emergence of high-quality large language models (LLMs) and various effective training methodologies.
Learning Equi-angular Representations for Online Continual Learning
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e. g., single-epoch training).
