Translating Pro-Drop Languages with Reconstruction Models
Table 7: Translation results when reconstruction is used in training only while not used in testing.
['[BOLD] Model', '[BOLD] Test', '△']
As mentioned previously, the effect of reconstruction is two-fold: (1) it improves the training of baseline parameters, which leads to better hidden representations that embed labelled DP information learned from the training data; and (2) it serves as a reranking metric in testing to measure the quality of DP translation. Results show all variants to outperform the baseline models and applying reconstructors to both sides achieves the best performance overall. This is encouraging, since no extra resources nor computation are introduced to online decoding, making the approach highly practical, for example for translation in industry applications.