再论往返翻译的质量评估
Revisiting Round-Trip Translation for Quality Estimation
摘要：
质量评估（QE）是在没有人工翻译参考的情况下自动评估翻译质量的任务。计算输入句子和往返翻译（RTT）之间的BLEU曾经被认为是QE的一个指标，然而，它被发现是一个差的翻译质量预测。最近，各种预先训练的语言模型通过提供语义上有意义的单词和句子嵌入在NLP任务中取得了突破。在本文中，我们采用语义嵌入RTT为基础的QE。与之前提交的WMT 2019质量估计指标任务相比，我们的方法与人类判断的相关性最高。虽然向后翻译模型可以是一个缺点，当使用RTT，我们观察到，语义级的指标，基于RTT的QE是强大的向后翻译系统的选择。此外，所提出的方法在SMT和NMT前向翻译系统中表现出一致的性能，这意味着该方法不会惩罚某种类型的模型。
英文摘要：
Quality estimation (QE) is the task of automatically evaluating the quality of translations without human-translated references. Calculating BLEU between the input sentence and round-trip translation (RTT) was once considered as a metric for QE, however, it was found to be a poor predictor of translation quality. Recently, various pre-trained language models have made breakthroughs in NLP tasks by providing semantically meaningful word and sentence embeddings. In this paper, we employ semantic embeddings to RTT-based QE. Our method achieves the highest correlations with human judgments, compared to previous WMT 2019 quality estimation metric task submissions. While backward translation models can be a drawback when using RTT, we observe that with semantic-level metrics, RTT-based QE is robust to the choice of the backward translation system. Additionally, the proposed method shows consistent performance for both SMT and NMT forward translation systems, implying the method does not penalize a certain type of model.
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。
