Multi-source Neural Automatic Post-Editing: FBK's participation in the WMT 2017 APE shared task

Rajen Chatterjee; M. Amin Farajian; Matteo Negri; Marco Turchi; Ankit Srivastava; Santanu Pal

In: Second Conference on Machine Translation - Proceedings. Conference on Machine Translation (WMT-2017), September 7-8, Copenhagen, Denmark, Pages 630-638, Association for Computational Linguistics, 9/2017.


Previous phrase-based approaches to Au- tomatic Post-editing (APE) have shown that the dependency of MT errors from the source sentence can be exploited by jointly learning from source and target information. By integrating this notion in a neural approach to the problem, we present the multi-source neural machine translation (NMT) system submitted by FBK to the WMT 2017 APE shared task. Our system implements multi-source NMT in a weighted ensemble of 8 models. The n-best hypotheses produced by this ensemble are further re-ranked using features based on the edit distance between the original MT output and each APE hypothesis, as well as other statistical models (n-gram language model and operation sequence model). This solution resulted in the best system submission for this round of the APE shared task for both en-de and de-en language directions. For the former language direction, our primary submission improves over the MT baseline up to -4.9 TER and +7.6 BLEU points. For the latter, where the higher quality of the original MT output reduces the room for improvement, the gains are lower but still significant (-0.25 TER and +0.3 BLEU).

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WMT73.pdf (pdf, 195 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence