A Transformer-Based Multi-Source Automatic Post-Editing System

Santanu Pal; Nico Herbig; Antonio Krüger; Josef van Genabith

In: Proceedings of the Third Conference on Machine Translation. Conference on Machine Translation (WMT-2018), October 31 - November 1, Brussels, Belgium, Association for Computational Linguistics, 2018.


This paper presents our English--German Automatic Post-Editing (APE) system submitted to the APE Task organized at WMT 2018. The proposed model is an extension of the transformer architecture: two separate self-attention-based encoders encode the machine translation output (mt) and the source (src), followed by a joint encoder that attends over a combination of these two encoded sequences (enc_{src} and enc_{mt}) for generating the post-edited sentence. We compare this multi-source architecture (i.e, {src, mt} -> pe) to a monolingual transformer (i.e., mt -> pe) model and an ensemble combining the multi-source {src, mt} -> pe and single-source mt -> pe models. For both the PBSMT and the NMT task, the ensemble yields the best results, followed by the multi-source model and last the single-source approach. Our best model, the ensemble, achieves a BLEU score of 66.16 and 74.22 for the PBSMT and NMT task, respectively.


Weitere Links

wmt-ape-18.pdf (pdf, 259 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence