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.
翻译:本文建议将预先培训的语言模型与开放域对话模型模块式对话模式结合起来。 我们的方法、 语义强化的精细调整、 即时对话理解、 规划和反应生成, 作为一种语言模型微调任务。 从推断来看, 我们分解语义和象征性变异, 具体指定每个模块的抽样方法和限制。 在培训和评估方面, 我们提出 X- Weibo, 中国多回合开放域对话数据集, 配有情感、 度量值和主题字词的自动注释。 实验显示, 语义强化的精细调整超越了非语义和语义指标的强大基线, 提高了人类评估的相关性、 一致性和 信息性, 并展示了对语义变量的可控性。