论文标题
语言模型作为单词顺序假设的替代评估者：日语的案例研究
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
论文作者
论文摘要
我们使用神经语言模型（LMS）来研究一种方法来分析语言顺序。这种基于LM的方法有可能克服现有方法所面临的困难，例如基于计数的方法中预处理器错误的传播。在这项研究中，我们探讨了基于LM的方法是否有效分析订单一词。作为一个案例研究，由于日语复杂而灵活的单词顺序，这项研究重点关注日本。为了验证基于LM的方法，我们测试了（i）LMS和人词顺序偏好之间的相似之处，以及（ii）使用基于LM的方法和以前的语言研究获得的结果的一致性。通过我们的实验，我们初步得出结论，LMS显示出足够的单词顺序知识作为分析工具。最后，使用基于LM的方法，我们证明了规范单词顺序和局部化之间的关系，这尚未通过大规模实验来分析。
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.
