運用少量語料於漢字轉客文字之類神經機器翻譯系統初步探討

A Preliminary Study on Mandarin-Hakka neural machine translation using small-sized data

在本研究中，我們利用注意力機制(Attention)與卷積(Convolution)模型的架構來實作一套中文轉四縣腔客文的機器翻譯系統。其中，為了解決南北四縣腔的常用詞差異問題，我們透過語料的統計整理與詞典的定義方式，將兩種腔調的差異用法獨立出來，並分別訓練翻譯模型。此外，為了解決客語語料數量稀少而導致翻譯時遇到未知詞的狀況，在我們的模型中，透過實驗的驗證尋找適當的閥值以拒絕掉不適合的翻譯結果，並透過中文詞的替換以及客文常用詞的強制斷詞方式，讓最終的翻譯句獲得更佳的結果。最終，本研究所開發的系統可在少量語料下達到不錯的翻譯結果，並可應用於在地化的客語教學以及作為中客文夾雜之語音合成系統的前端部分。

In this study, we implemented a machine translation system using the Convolutional Neural Network with Attention mechanism for translating Mandarin to Sixan-accent Hakka. Specifically, to cope with the different idioms or terms used between Northern and Southern Sixan-accent, we analyzed the corpus differences and lexicon definition, and then separated the various word usages for training exclusive models for each accent. Besides, since the collected Hakka corpora are relatively limited, the unseen words frequently occurred during real-world translation. In our system, we selected suitable thresholds for each model based on the model verification to reject non-suitable translated words. Then, by applying the proposed algorithm, which adopted the forced Hakka idioms/terms segmentation and the common Mandarin word substitution, the resultant translation sentences become more intelligible. Therefore, the proposed system achieved promising results using small-sized data. This system could be used for Hakka language teaching and also the front-end of Mandarin and Hakka code-switching speech synthesis systems.