Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling using a Two-Dimensional Grid
This work addresses the problem of efficiently performing bidirectional translation for researchers and practitioners by proposing a single model instead of two separate ones.
This paper proposes a single end-to-end bidirectional neural machine translation model using a two-dimensional grid, allowing for both source-to-target and target-to-source translation within one network. Experiments on WMT 2018 German$\leftrightarrow$English and Turkish$\leftrightarrow$English tasks demonstrate its ability to produce good translation quality.
Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not easy to get the best effect for bidirectional translation, i.e., both source-to-target and target-to-source translation using a single model. If we exclude some pioneering attempts, such as multilingual systems, all other bidirectional translation approaches are required to train two individual models. This paper proposes to build a single end-to-end bidirectional translation model using a two-dimensional grid, where the left-to-right decoding generates source-to-target, and the bottom-to-up decoding creates target-to-source output. Instead of training two models independently, our approach encourages a single network to jointly learn to translate in both directions. Experiments on the WMT 2018 German$\leftrightarrow$English and Turkish$\leftrightarrow$English translation tasks show that the proposed model is capable of generating a good translation quality and has sufficient potential to direct the research.