Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues
This work addresses the problem of generating more controllable and high-quality dialogues for users in conversational AI, though it is incremental as it builds on existing modular and finetuning approaches.
The paper tackles open-domain dialogue modeling by combining pretrained language models with a modular paradigm, resulting in improved performance on both non-semantic and semantic metrics, including enhanced human-evaluated relevance, coherence, and informativeness.
This paper propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiates conversation understanding, planning, and response generation as a language model finetuning task. At inference, we disentangle semantic and token variations by specifying sampling methods and constraints for each module separately. For training and evaluation, we present X-Weibo, a Chinese multi-turn open-domain dialogue dataset with automatic annotation for emotions, DAs, and topical words. Experiments show that semantic-enhanced finetuning outperforms strong baselines on non-semantic and semantic metrics, improves the human-evaluated relevance, coherence, and informativeness, and exhibits considerable controllability over semantic variables.