Investigating Backtranslation in Neural Machine Translation
A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a high-quality NMT system. Given that large collections of new parallel text become available only quite rarely, backtranslation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus -- both as a separate standalone dataset as well as combined with human-generated parallel data -- affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for German-to-English, and analyse the resulting translation performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTTranslationSimilar Papers 제목 키워드 기반
Boosting Neural Machine Translation from Finnish to Northern Sámi with Rule-Based Backtranslation
We consider a low-resource translation task from Finnish into Northern Sámi. Collecting all available parallel data between the languages, we obtain around 30,000 sentence pairs. However, there exists a significantly lar…
Machine TranslationNMTSentenceTranslationBacktranslation in Neural Morphological Inflection
Backtranslation is a common technique for leveraging unlabeled data in low-resource scenarios in machine translation. The method is directly applicable to morphological inflection generation if unlabeled word forms are a…
Machine TranslationMorphological InflectionTranslationExploring Parameter-Efficient Fine-Tuning and Backtranslation for the WMT 25 General Translation Task
In this paper, we explore the effectiveness of combining fine-tuning and backtranslation on a small Japanese corpus for neural machine translation. Starting from a baseline English{\textrightarrow}Japanese model (COMET =…
parameter-efficient fine-tuningMachine TranslationStudy on Unsupervised Statistical Machine Translation for Backtranslation
Machine Translation systems have drastically improved over the years for several language pairs. Monolingual data is often used to generate synthetic sentences to augment the training data which has shown to improve the …
Machine TranslationTranslationUnsupervised Machine Translation