Lexical alignment, where speakers start to use similar words across conversation, is known to contribute to successful communication. However, its implementation in conversational agents remains underexplored, particularly considering the recent advancements in large language models (LLMs). As a first step towards enabling lexical alignment in human-agent dialogue, this study draws on strategies for personalising conversational agents and investigates the construction of stable, personalised lexical profiles as a basis for lexical alignment. Specifically, we varied the amounts of transcribed spoken data used for construction as well as the number of items included in the profiles per part-of-speech (POS) category and evaluated profile performance across time using recall, coverage, and cosine similarity metrics. It was shown that smaller and more compact profiles, created after 10 min of transcribed speech containing 5 items for adjectives, 5 items for conjunctions, and 10 items for adverbs, nouns, pronouns, and verbs each, offered the best balance in both performance and data efficiency. In conclusion, this study offers practical insights into constructing stable, personalised lexical profiles, taking into account minimal data requirements, serving as a foundational step toward lexical alignment strategies in conversational agents.

翻译：词汇对齐，即对话者开始使用相似词汇的现象，已被证实有助于成功沟通。然而，其在对话智能体中的实现仍待深入探索，尤其是在大型语言模型（LLMs）近期取得进展的背景下。作为实现人机对话中词汇对齐的第一步，本研究借鉴了对话智能体个性化策略，探究了构建稳定、个性化的词汇档案作为词汇对齐的基础。具体而言，我们通过改变用于构建档案的转录口语数据量以及每个词性（POS）类别中包含的条目数量，并利用召回率、覆盖率和余弦相似度指标评估档案随时间变化的性能。结果表明，基于10分钟转录语音构建的较小且更紧凑的档案——其中形容词和连词各包含5个条目，副词、名词、代词和动词各包含10个条目——在性能和数据效率方面达到了最佳平衡。总之，本研究为构建稳定、个性化的词汇档案提供了实用见解，同时考虑了最小数据需求，为对话智能体中的词汇对齐策略奠定了初步基础。