Can Automatic Post-Editing Improve NMT?

By Shamil Chollampatt, Raymond Hendy Susanto, Liling Tan, Ewa Szymańska. 2020

29 citations recorded by the source corpus.

Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort. APE has had notable success when used with statistical machine translation (SMT) systems but has not been as successful over neural machine translation (NMT) systems. This has raised questions on the relevance of APE task in the current scenario. However, the training of APE models has been heavily reliant on large-scale artificial corpora combined with only limited human post-edited data. We hypothesize that APE models have been underperforming in improving NMT translations due to the lack of adequate supervision. To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT. We empirically show that a state-of-art neural APE model trained on this corpus can significantly improve a strong in-domain NMT system, challenging the current understanding in the field. We further investigate the effects of varying training data sizes, using artificial training data, and domain specificity for the APE task. We release this new corpus under CC BY-NC-SA 4.0 license at this https URL.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “Text editing is a decades-old subfield of NLP that has traditionally focused on fixing errors in machine translation (Chollampatt et al., 2020; Simard et al., 2007; Chatterjee et al., 2020) or grammar in human-written text (Wang et al., 2020b).”