Repairing Pronouns in Translation with BERT-Based Post-Editing
Pronouns are important determinants of a text's meaning but difficult to translate. This is because pronoun choice can depend on entities described in previous sentences, and in some languages pronouns may be dropped when the referent is inferrable from the context. These issues can lead Neural Machine Translation (NMT) systems to make critical errors on pronouns that impair intelligibility and even reinforce gender bias. We investigate the severity of this pronoun issue, showing that (1) in some domains, pronoun choice can account for more than half of a NMT systems' errors, and (2) pronouns have a disproportionately large impact on perceived translation quality. We then investigate a possible solution: fine-tuning BERT on a pronoun prediction task using chunks of source-side sentences, then using the resulting classifier to repair the translations of an existing NMT model. We offer an initial case study of this approach for the Japanese-English language pair, observing that a small number of translations are significantly improved according to human evaluators.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation
Machine translation systems with inadequate document understanding can make errors when translating dropped or neutral pronouns into languages with gendered pronouns (e.g., English). Predicting the underlying gender of t…
document understandingMachine TranslationSentenceTranslationInteractive Post-Editing for Verbosity Controlled Translation
We explore Interactive Post-Editing (IPE) models for human-in-loop translation to help correct translation errors and rephrase it with a desired style variation. We specifically study verbosity for style variations and b…
Machine TranslationTranslationDiscourse Dependency: A Continuous Criterion for Translation Difficulty
Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeasured axis is referential reach, the distance a segment must look bac…
Machine TranslationUnbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing
This paper describes Unbabel's submission to the WMT2019 APE Shared Task for the English-German language pair. Following the recent rise of large, powerful, pre-trained models, we adapt the BERT pretrained model to perfo…
Automatic Post-EditingDecoderMachine TranslationNMT+1A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits. APE systems are usually trained by complementing human post-edited data with …
Automatic Post-EditingDecoderGPUMachine Translation+2