Improving Decoding Generalization for Tree-to-String Translation

Abstract To address the parse error issue for tree-tostring translation, this paper proposes a similarity-based decoding generation (SDG) solution by reconstructing similar source parse trees for decoding at the decoding time instead of taking multiple source parse trees as input for decoding. Experiments on Chinese-English translation demonstrated that our approach can achieve a significant improvement over the standard method, and has little impact on decoding speed in practice. Our approach is very easy to implement, and can be applied to other paradigms such as tree-to-tree models