Say Anything：L2英语不定代词的自动语义错误检测
Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns
摘要：
第二语言使用者错误检测的计算研究主要针对初学者到中级水平的学习者典型的明显语法异常。相反，我们专注于收购微妙的语义细微差别的英语不定代词的非母语人士在不同水平的熟练程度。首先，我们提出了理论上的，语言学动机的假设，并支持实证证据的性质所构成的挑战，不定代词的英语学习者。然后，我们建议并评估一种自动检测非典型使用模式的方法，证明深度学习架构对于涉及细微语义异常的任务是有希望的。
英文摘要：
Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level. We focus instead on acquisition of subtle semantic nuances of English indefinite pronouns by non-native speakers at varying levels of proficiency. We first lay out theoretical, linguistically motivated hypotheses, and supporting empirical evidence on the nature of the challenges posed by indefinite pronouns to English learners. We then suggest and evaluate an automatic approach for detection of atypical usage patterns, demonstrating that deep learning architectures are promising for this task involving nuanced semantic anomalies.
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。
