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We examine a methodology using neural language models (LMs) for analyzing theword order of language. This LM-based method has the potential to overcome thedifficulties existing methods face, such as the propagation of preprocessorerrors in count-based methods. In this study, we explore whether the LM-basedmethod is valid for analyzing the word order. As a case study, this studyfocuses on Japanese due to its complex and flexible word order. To validate theLM-based method, we test (i) parallels between LMs and human word orderpreference, and (ii) consistency of the results obtained using the LM-basedmethod with previous linguistic studies. Through our experiments, wetentatively conclude that LMs display sufficient word order knowledge for usageas an analysis tool. Finally, using the LM-based method, we demonstrate therelationship between the canonical word order and topicalization, which had yetto be analyzed by large-scale experiments.

Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese