Notwithstanding recent advances, syntactic generalization remains a challenge for text decoders. While some studies showed gains from incorporating source-side symbolic syntactic and semantic structure into text generation Transformers, very little work addressed the decoding of such structure. We propose a general approach for tree decoding using a transition-based approach. Examining the challenging test case of incorporating Universal Dependencies syntax into machine translation, we present substantial improvements on test sets that focus on syntactic generalization, while presenting improved or comparable performance on standard MT benchmarks. Further qualitative analysis addresses cases where syntactic generalization in the vanilla Transformer decoder is inadequate and demonstrates the advantages afforded by integrating syntactic information.

翻译：尽管最近取得了一些进展,但对文本解密者来说,合成法的概括化仍然是一项挑战。虽然一些研究表明在将源侧象征性符号合成和语义结构纳入文本生成变异器方面有所收获,但很少涉及这种结构的解码问题。我们建议采用基于过渡的方法对树进行解码的一般办法。我们研究了将普遍依赖性合成法纳入机器翻译这一具有挑战性的测试案例,我们提出了大量改进的测试组,该测试组侧重于综合法的概括化,同时介绍了在标准的MT基准方面的改进或可比绩效。进一步的定性分析涉及香草变异器解码器的合成概括化不够充分的情况,并展示了整合合成信息的好处。