Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model
Table 1: Comparison of the methods together with different hidden dimension size (H) for all datasets.
['Method', 'H', 'En → De Perplexity', 'En → De BLEU', 'En → Ch Perplexity', 'En → Ch BLEU', 'En → Fa Perplexity', 'En → Fa BLEU']
In all cases, our work achieved higher BLEU score (up to 0.7 score) compared to the two other models for all of the datasets. Therefore, it can be inferred that using grammar forests can compensate the errors and ambiguities of parsers.