语言模型作为语序假设的替代评估工具--以日语为例
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
摘要：
我们研究了一种方法，使用神经语言模型（LM）分析语言的词序。这种基于LM的方法有可能克服现有方法面临的困难，如传播的预处理器的错误计数为基础的方法。在这项研究中，我们探讨是否LM为基础的方法是有效的分析词序。作为一个个案研究，本研究的重点是日语，因为它的复杂和灵活的词序。为了验证基于LM的方法，我们测试（i）LM和人类词序偏好之间的相似性，以及（ii）使用基于LM的方法与以前的语言学研究所获得的结果的一致性。通过我们的实验，我们初步得出结论，LM显示足够的词序知识作为一种分析工具。最后，我们使用基于LM的方法，证明了规范词序和主题化之间的关系，这还有待于通过大规模的实验来分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
