The main problem addressed in the paper is the limited availability of parallel data for training Neural Machine Translation (NMT) systems. NMT models require large amounts of parallel data to achieve good translation quality, but such data may be scarce for certain language pairs or domains. Using monolingual data to perform neural machine translation is difficult. By using back-translation, we are able to generate synthetic corpora, which we can append onto the original dataset.
Proposed Method
The paper introduces the Iterative Back-Translation (IBT) method as a solution to improve NMT performance in scenarios with limited parallel data. IBT involves generating synthetic parallel data by back-translating target monolingual data into the source language. The proposed method iteratively refines the back-translation process, improving translation quality with each iteration. Simply, this means performing back-translation on previously back-translated data. Techniques such as length control, confidence thresholding, and model selection are introduced to enhance the back-translation process.
Input/Output
The input to the proposed method is limited parallel data, consisting of source sentences and their translations, along with monolingual data in both the source and target languages. The output is an iteratively refined NMT model, which is trained on augmented datasets comprising the original limited parallel data and the synthetic parallel data generated through back-translation.
Example
The paper presents experimental results on various language pairs, including German-English, English-French, and English-Farsi. It demonstrates the effectiveness of the IBT method in improving translation quality. For instance, in the German-English high-resource scenario, the final IBT systems outperform baseline NMT models and achieve the best reported BLEU scores for the WMT 2017 task.
Related Works &Their Gaps
The paper discusses related works such as traditional back-translation approaches in statistical machine translation and modern NMT research. It mentions previous studies that explored the use of monolingual data, linguistic constraints, or separately trained language models. However, it highlights the gap that these approaches have not proven to be as successful as back-translation in leveraging monolingual data for improving translation quality. The paper also mentions the dual learning approach, but notes that it did not achieve significant gains in their experiments. Thus, the proposed IBT method fills the gap by providing a simple and effective approach for leveraging back-translation to enhance NMT systems.
Main problem
The main problem addressed in the paper is the limited availability of parallel data for training Neural Machine Translation (NMT) systems. NMT models require large amounts of parallel data to achieve good translation quality, but such data may be scarce for certain language pairs or domains. Using monolingual data to perform neural machine translation is difficult. By using back-translation, we are able to generate synthetic corpora, which we can append onto the original dataset.
Proposed Method
The paper introduces the Iterative Back-Translation (IBT) method as a solution to improve NMT performance in scenarios with limited parallel data. IBT involves generating synthetic parallel data by back-translating target monolingual data into the source language. The proposed method iteratively refines the back-translation process, improving translation quality with each iteration. Simply, this means performing back-translation on previously back-translated data. Techniques such as length control, confidence thresholding, and model selection are introduced to enhance the back-translation process.
Input/Output
The input to the proposed method is limited parallel data, consisting of source sentences and their translations, along with monolingual data in both the source and target languages. The output is an iteratively refined NMT model, which is trained on augmented datasets comprising the original limited parallel data and the synthetic parallel data generated through back-translation.
Example
The paper presents experimental results on various language pairs, including German-English, English-French, and English-Farsi. It demonstrates the effectiveness of the IBT method in improving translation quality. For instance, in the German-English high-resource scenario, the final IBT systems outperform baseline NMT models and achieve the best reported BLEU scores for the WMT 2017 task.
Related Works &Their Gaps
The paper discusses related works such as traditional back-translation approaches in statistical machine translation and modern NMT research. It mentions previous studies that explored the use of monolingual data, linguistic constraints, or separately trained language models. However, it highlights the gap that these approaches have not proven to be as successful as back-translation in leveraging monolingual data for improving translation quality. The paper also mentions the dual learning approach, but notes that it did not achieve significant gains in their experiments. Thus, the proposed IBT method fills the gap by providing a simple and effective approach for leveraging back-translation to enhance NMT systems.