Modeling Fluency and Faithfulness for Diverse Neural Machine Translation
Table 4: N-gram accuracy and cosine similarity on CN\rightarrowEN translation. N-gram accuracy is the average on all the test sets. Cosine similarity is calculated with the average embeddings of the translation against that of the reference.
['[EMPTY]', '[BOLD] 1-gram', '[BOLD] 2-gram', '[BOLD] 3-gram', '[BOLD] 4-gram', '[BOLD] Cosine']
Higher 1-gram accuracy indicates that our method can reach more ground truth in optimization and this is another proof that the loss we use can lead to better optima. Besides, the greater accuracy on n-gram, especially on 3-gram and 4-gram, implies better fluency. Furthermore, our method can have a bigger cosine similarity to the reference and this means the generate translation is more faithful in meaning to the source sentence. In conclusion, our method can produce translation with better fluency and faithfulness.