论文标题
针对任务对话系统的语言模型的比较研究
A Comparative Study on Language Models for Task-Oriented Dialogue Systems
论文作者
论文摘要
语言模型的最新发展通过微调预验证的模型来实现各种自然语言任务的最新表现，从而显示出令人鼓舞的结果。在面向任务的对话（TOD）系统中，语言模型可以用于端到端培训，而无需依靠对话状态跟踪来跟踪对话历史记录，但允许语言模型根据给出的输入的上下文产生响应。本文进行了一项比较研究，以显示在终端TOD系统上使用最近预审预周仔的微调模型（例如BART和T5）的有效性和强度。实验结果表明，语言模型微调后进行了实质性改进。这些模型在将知识添加到上下文中产生更多流利的响应，以指导模型避免幻觉并在生成的响应中产生准确的实体。此外，我们发现BART和T5在BLEU和F1分数中的模型优于基于GPT的模型，并在TOD系统中实现了最先进的性能。
The recent development of language models has shown promising results by achieving state-of-the-art performance on various natural language tasks by fine-tuning pretrained models. In task-oriented dialogue (ToD) systems, language models can be used for end-to-end training without relying on dialogue state tracking to track the dialogue history but allowing the language models to generate responses according to the context given as input. This paper conducts a comparative study to show the effectiveness and strength of using recent pretrained models for fine-tuning, such as BART and T5, on endto-end ToD systems. The experimental results show substantial performance improvements after language model fine-tuning. The models produce more fluent responses after adding knowledge to the context that guides the model to avoid hallucination and generate accurate entities in the generated responses. Furthermore, we found that BART and T5 outperform GPT-based models in BLEU and F1 scores and achieve state-of-the-art performance in a ToD system.
