In second language learning, scenario-based conversation practice is
important for language learners to achieve fluency in speaking, but students
often lack sufficient opportunities to practice their conversational skills
with qualified instructors or native speakers. To bridge this gap, we propose
situational dialogue models for students to engage in conversational practice.
Our situational dialogue models are fine-tuned on large language models (LLMs),
with the aim of combining the engaging nature of an open-ended conversation
with the focused practice of scenario-based tasks. Leveraging the
generalization capabilities of LLMs, we demonstrate that our situational
dialogue models perform effectively not only on training topics but also on
topics not encountered during training. This offers a promising solution to
support a wide range of conversational topics without extensive manual work.
Additionally, research in the field of dialogue systems still lacks reliable
automatic evaluation metrics, leading to human evaluation as the gold standard
(Smith et al., 2022), which is typically expensive. To address the limitations
of existing evaluation methods, we present a novel automatic evaluation method
that employs fine-tuned LLMs to efficiently and effectively assess the
performance of situational dialogue models.

在第二语言学习中，基于情景的对话实践对于语言学习者实现口语流利至关重要，本文提出了基于情境的对话模型，通过在大型语言模型（LLMs）上进行微调，旨在结合开放式对话的吸引力和基于情景任务的集中练习。利用 LLMs 的泛化能力，我们证明了我们的情境对话模型在训练话题和未遇到的话题上都有效，为支持广泛的会话主题提供了有希望的解决方案。此外，对话系统领域的研究仍缺乏可靠的自动评估指标，引发人工评估作为黄金标准（Smith 等，2022）的问题，而且通常成本较高。为了解决现有评估方法的局限性，我们提出了一种新颖的自动评估方法，利用经过微调的 LLMs 来高效而有效地评估情境对话模型的性能。