论文标题
神经机器翻译的句子扩展鲁棒性
Sentence Boundary Augmentation For Neural Machine Translation Robustness
论文作者
论文摘要
神经机器翻译（NMT）模型在提供了良好的培训和评估数据的翻译任务上表现出强烈的最新表现状态，但它们仍然对包括各种类型错误的输入敏感。具体而言，在输入转录物来自自动语音识别（ASR）的长形语音翻译系统的背景下，NMT模型必须处理包括音素替代，语法结构和句子边界在内的错误，所有这些都对NMT鲁棒性构成了挑战。通过深入的错误分析，我们表明句子边界分割对质量具有最大的影响，并且我们制定了一种简单的数据增强策略来提高分割的鲁棒性。
Neural Machine Translation (NMT) models have demonstrated strong state of the art performance on translation tasks where well-formed training and evaluation data are provided, but they remain sensitive to inputs that include errors of various types. Specifically, in the context of long-form speech translation systems, where the input transcripts come from Automatic Speech Recognition (ASR), the NMT models have to handle errors including phoneme substitutions, grammatical structure, and sentence boundaries, all of which pose challenges to NMT robustness. Through in-depth error analysis, we show that sentence boundary segmentation has the largest impact on quality, and we develop a simple data augmentation strategy to improve segmentation robustness.
