Multilingual Transitivity and Bidirectional Multilingual Agreement for Multilingual Document-level Machine Translation
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Multilingual machine translation has been proven an effective strategy to support translation between multiple languages with a single model. However, most studies focus on multilingual sentence translation without considering generating long documents across different languages, which requires an understanding of multilingual context dependency and is typically harder. In this paper, we first spot that naively incorporating auxiliary multilingual data either auxiliary-target or source-auxiliary brings no improvement to the source-target language pair in our interest. Motivated by this observation, we propose a novel framework called Multilingual Transitivity (MTrans) to find an implicit optimal route via source-auxiliary-target within the multilingual model. To encourage MTrans, we propose a novel method called Triplet Parallel Data (TPD), which uses parallel triplets that contain (source-auxiliary, auxiliary-target, and source-target) for training. The auxiliary language then serves as a pivot and automatically facilitates the implicit information transition flow which is easier to translate. We further propose a novel framework called Bidirectional Multilingual Agreement (Bi-MAgree) that encourages the bidirectional agreement between different languages. To encourage Bi-MAgree, we propose a novel method called Multilingual Kullback-Leibler Divergence (MKL) that forces the output distribution of the inputs with the same meaning but in different languages to be consistent with each other. The experimental results indicate that our methods bring consistent improvements over strong baselines on three document translation tasks: IWSLT2015 Zh-En, De-En, and Vi-En. Our analysis validates the usefulness and existence of MTrans and Bi-MAgree, and our frameworks and methods are effective on synthetic auxiliary data.
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