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

• URL: http://arxiv.org/abs/2005.00842v1
• Date: Sat, 2 May 2020 14:32:40 GMT
• Title: Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
• Authors: Tatsuki Kuribayashi, Takumi Ito, Jun Suzuki, Kentaro Inui
• Abstract summary: We examine a methodology using neural language models (LMs) for analyzing the word order of language. We explore whether the LM-based method is valid for analyzing the word order. We conclude that LMs display sufficient word order knowledge for usage as an analysis tool.
• Score: 45.80297329300326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parallels between LMs and human word order preference, and (ii) consistency of the results obtained using the LM-based method with previous linguistic studies. Through our experiments, we tentatively conclude that LMs display sufficient word order knowledge for usage as an analysis tool. Finally, using the LM-based method, we demonstrate the relationship between the canonical word order and topicalization, which had yet to be analyzed by large-scale experiments.

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