Simulation of Language Evolution based on Actual Diachronic Change Extracted from Legal Terminology

摘要

Simulation studies have played an important role in language evolution. Although a variety of methodologies have been proposed so far, they are typically too abstract to recognize that their learning mechanisms properly reflect actual ones. One reason comes from the lack of empirical data recorded for a long period with explicit description. Our purpose in this paper is to show simulation models adapt to actual language change. As empirical diachronic data, we focus on a statutory corpus. In general, statutes define important legal terms with explanatory sentences, which are also revised by amendment. We proposed an iterated learning model, in which an infant agent learns grammar through his/her parent's utterances about legal terms and their semantic relations, and the infant becomes a parent in the next generation. The key issue is that the learning situation about legal terms and their relations can be changed due to amendment. Our experimental result showed that infant agents succeeded to acquire compositional grammar despite irregular changes in their learning situation.