论文标题
通过UCCA将语义结构纳入机器翻译评估
Incorporate Semantic Structures into Machine Translation Evaluation via UCCA
论文作者
论文摘要
复制机制通常用于神经释义网络和其他文本生成任务中，其中输入序列中的一些重要单词保留在输出序列中。同样，在机器翻译中，我们注意到一个源文本的所有良好翻译中都出现了某些单词或短语，这些单词倾向于传达重要的语义信息。因此，在这项工作中，我们将句子中具有重要语义含义的单词定义为语义核心词。此外，我们提出了一种称为语义加权句子相似性（SWSS）的MT评估方法。它利用UCCA的力量来识别语义核心词，然后计算语义核心词的重叠的句子相似性得分。实验结果表明，SWS可以一致地改善基于词汇相似性的流行MT评估指标的性能。
Copying mechanism has been commonly used in neural paraphrasing networks and other text generation tasks, in which some important words in the input sequence are preserved in the output sequence. Similarly, in machine translation, we notice that there are certain words or phrases appearing in all good translations of one source text, and these words tend to convey important semantic information. Therefore, in this work, we define words carrying important semantic meanings in sentences as semantic core words. Moreover, we propose an MT evaluation approach named Semantically Weighted Sentence Similarity (SWSS). It leverages the power of UCCA to identify semantic core words, and then calculates sentence similarity scores on the overlap of semantic core words. Experimental results show that SWSS can consistently improve the performance of popular MT evaluation metrics which are based on lexical similarity.
