Authors:
Makoto Nakamura
1
;
Yuya Hayashi
2
and
Ryuichi Matoba
2
Affiliations:
1
Nagoya University, Japan
;
2
National Institute of Technology, Japan
Keyword(s):
Language Evolution, Simulation, Iterated Learning Model, Cognitive Bias, Statute, Legal Terminology.
Related
Ontology
Subjects/Areas/Topics:
Agent Communication Languages
;
Agents
;
Artificial Intelligence
Abstract:
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 suc
ceeded to acquire compositional grammar despite irregular changes in their learning situation.
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