论文标题
减少与长度感知框架的同时机器翻译中的位置偏差
Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework
论文作者
论文摘要
同时机器翻译（SIMT）在接收流源输入时开始翻译，因此源句子在翻译过程中总是不完整。与使用常规SEQ-to-seq体系结构的全句MT不同，Simt经常应用前缀到排序架构，该架构迫使每个目标单词仅与部分源前缀对齐以适应流输入中不完整的源。但是，正面位置中的源单词总是被认为更为重要，因为它们出现在更多的前缀中，从而导致位置偏差，这使得该模型更加关注测试的前源位置。在本文中，我们首先分析SIMT中位置偏差的现象，并开发一个长度感知的框架来减少位置偏置，通过弥合Simt和全句MT之间的结构间隙。具体而言，在流媒体输入的情况下，我们首先预测全句子长度，然后用位置编码填充未来的源位置，从而将流入输入变成伪全句。提出的框架可以集成到大多数现有的SIMT方法中，以进一步提高性能。包括最先进的自适应策略在内的两种代表性SIMT方法的实验表明，我们的方法成功地降低了位置偏差，从而实现了更好的SIMT性能。
Simultaneous machine translation (SiMT) starts translating while receiving the streaming source inputs, and hence the source sentence is always incomplete during translating. Different from the full-sentence MT using the conventional seq-to-seq architecture, SiMT often applies prefix-to-prefix architecture, which forces each target word to only align with a partial source prefix to adapt to the incomplete source in streaming inputs. However, the source words in the front positions are always illusoryly considered more important since they appear in more prefixes, resulting in position bias, which makes the model pay more attention on the front source positions in testing. In this paper, we first analyze the phenomenon of position bias in SiMT, and develop a Length-Aware Framework to reduce the position bias by bridging the structural gap between SiMT and full-sentence MT. Specifically, given the streaming inputs, we first predict the full-sentence length and then fill the future source position with positional encoding, thereby turning the streaming inputs into a pseudo full-sentence. The proposed framework can be integrated into most existing SiMT methods to further improve performance. Experiments on two representative SiMT methods, including the state-of-the-art adaptive policy, show that our method successfully reduces the position bias and thereby achieves better SiMT performance.
