Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance in recent years. However, the under-translation and over-translation problem still remain a big challenge. Through error analysis, we find that under-translation is much more prevalent than over-translation and the source words that need to be reordered during translation are more likely to be ignored. To address the under-translation problem, we explore the pre-ordering approach for NMT. Specifically, we pre-order the source sentences to approximate the target language word order. We then combine the pre-ordering model with position embedding to enhance the monotone translation. Finally, we augment our model with the coverage mechanism to tackle the over-translation problem. Experimental results on Chinese-to-English translation have shown that our method can significantly improve the translation quality by up to 2.43 BLEU points. Furthermore, the detailed analysis demonstrates that our approach can substantially reduce the number of under-translation cases by 30.4% (compared to 17.4% using the coverage model).
@InProceedings{ZHAO18.129, author = {Yang Zhao and Jiajun Zhang and Chengqing Zong}, title = "{Exploiting Pre-Ordering for Neural Machine Translation}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper