Abstract
Most of modern neural machine translation (NMT) models are based on an encoder-decoder framework with an attention mechanism. While they perform well on standard datasets, they can have trouble in translation of long inputs that are rare or unseen during training. Incorporating target syntax is one approach to dealing with such length-related problems. We propose a novel syntactic decoder that generates a target-language dependency tree in a top-down, left-to-right order. Experiments show that the proposed top-down string-to-tree decoding generalizes better than conventional sequence-to-sequence decoding in translating long inputs that are not observed in the training data.
Abstract (translated)
大多数现代神经机器翻译（NMT）模型基于带有注意力机制的编码器-解码器框架。尽管它们在标准数据集上表现良好，但在翻译训练中罕见或未见的长输入时可能遇到困难。引入目标语言句法信息是处理此类长度相关问题的途径之一。我们提出了一种新颖的句法解码器，该解码器以自顶向下、从左到右的顺序生成目标语言依赖树。实验表明，所提出的自顶向下字符串转树解码在翻译训练数据中未观察到的长输入时，比传统的序列到序列解码具有更好的泛化能力。
URL
https://arxiv.org/abs/2603.27938
