Title: How do lexical semantics affect translation? An empirical study Show Chinese title

Title: 词汇语义如何影响翻译？ 实证研究

Abstract: Neural machine translation (NMT) systems aim to map text from one language into another. While there are a wide variety of applications of NMT, one of the most important is translation of natural language. A distinguishing factor of natural language is that words are typically ordered according to the rules of the grammar of a given language. Although many advances have been made in developing NMT systems for translating natural language, little research has been done on understanding how the word ordering of and lexical similarity between the source and target language affect translation performance. Here, we investigate these relationships on a variety of low-resource language pairs from the OpenSubtitles2016 database, where the source language is English, and find that the more similar the target language is to English, the greater the translation performance. In addition, we study the impact of providing NMT models with part of speech of words (POS) in the English sequence and find that, for Transformer-based models, the more dissimilar the target language is from English, the greater the benefit provided by POS. Show Chinese abstract

Abstract: 神经机器翻译（NMT）系统旨在将一种语言的文本映射到另一种语言。 虽然NMT有广泛的应用，但最重要的应用之一是自然语言的翻译。 自然语言的一个显著特点是，单词通常按照给定语言的语法规则进行排序。 尽管在开发用于翻译自然语言的NMT系统方面取得了许多进展，但很少有研究探讨源语言和目标语言的词序以及词汇相似性如何影响翻译性能。 在此，我们研究了来自OpenSubtitles2016数据库的多种低资源语言对中的这些关系，其中源语言是英语，并发现目标语言越接近英语，翻译性能就越高。 此外，我们研究了向NMT模型提供英语序列中单词词性（POS）的影响，并发现对于基于Transformer的模型，目标语言与英语越不相似，POS带来的好处就越大。