VerbNet—the most extensive online verb lexicon currently available for English—has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper.

中文翻译：

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研究VerbNet样式分类的跨语言可翻译性。

VerbNet是目前可用于英语的功能最广泛的在线动词词典，已证明对支持各种NLP任务很有用。但是，由于此类分类仅适用于少数几种语言，因此限制了其在多语言NLP中的开发。由于VerbNet的手动开发是一项主要任务，因此研究人员最近将VerbNet的类从英语翻译为其他语言。但是，尚未对这种翻译方法在不同类型的不同语言中的适用性和准确性进行系统的调查。我们的研究旨在填补这一空白。我们开发了一种系统的方法，用于将VerbNet类从英语翻译为其他语言，我们首先将其应用于波兰语，然后将其应用于克罗地亚语，普通话，日语，意大利语和芬兰语。我们在波兰语中的结果表明，所有类别的翻译都具有很高的可翻译性（96％的英语成员动词已成功翻译成波兰语），并且注释者之间也达成了强有力的共识，从而揭示了最终分类中重叠的可能性。在其他语言上的结果同样具有希望。这证明了VerbNet类具有强大的跨语言潜力，并且所提出的方法可用于获得不同语言自动动词分类的金标准。我们在本文中提供了注释准则和六种特定于语言的动词分类。在其他语言上的结果同样具有希望。这证明了VerbNet类具有强大的跨语言潜力，并且所提出的方法可用于获得不同语言自动动词分类的金标准。我们在本文中提供了注释准则和六种特定于语言的动词分类。在其他语言上的结果同样具有希望。这证明了VerbNet类具有强大的跨语言潜力，并且所提出的方法可用于获得不同语言自动动词分类的金标准。我们在本文中提供了注释准则和六种特定于语言的动词分类。