A Triple Unified Transformer for Context-Aware Dialogue Translation

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題名:	A Triple Unified Transformer for Context-Aware Dialogue Translation
作者:	林育全;Lin, Yu-Cyuan
貢獻者:	資訊管理學系
關鍵詞:	聊天翻譯;機器翻譯;深度學習;dialogue translation;machine translation;deep learning
日期:	2022-08-26
上傳時間:	2022-10-04 12:04:07 (UTC+8)
出版者:	國立中央大學
摘要:	對話翻譯任務旨在翻譯不同語言的人之間的對話文本。然而，現今在處理機器翻譯任務大多都使用句子級別的翻譯模型，但是這樣的架構卻無法有效地捕捉上下文的關係，會造成在跨語句的語意無法以正確的方式表達。然而文檔級別的翻譯架構可以處理上述的問題。雖然可以使用文檔級別的翻譯模型去應用在聊天翻譯的任務上，但是該如何選擇有用的上下文去幫助當前要翻譯的句子卻還是有待討論的議題。除此之外，也較少研究關注歷史聊天紀錄對翻譯效果的影響，這些資訊也是能有效幫助到當前要翻譯的句子，所以我們認為應該將其納入考慮。因此，我們提出了一個新的上下文選擇方式和一個適應聊天翻譯的模型，分別叫做United method和Triple-Unified-Transformer。我們的模型可以更好的學習到跨句子之間的關係，使當前要翻譯的句子得到更好的結果。在實驗一中，我們使用三個不同的聊天翻譯資料集和自動化的評量指標去衡量我們提出的United method和Triple-Unified-Transformer的效果。實驗結果顯示，Flat-Transformer再加入United method後的表現是能提升翻譯效果的。另一方面，Triple-Unified-Transformer的翻譯效果在特定資料集可以得到不錯的結果 (BLEU4、BLONDE)。此外，在實驗二中，我們測試United method和Triple-Unified-Transformer是否具有一般性，可以適應在不同的聊天情境中。實驗結果顯示，Triple-Unified-Transformer在特定的資料集中表現最好，也代表說我們提出的模型是有能力適用在不同的聊天情境中，而有加入United method的基線模型也都能有較好的表現。 ;Dialogue translation is designed to translate the text of conversations between people of different languages. Most of machine translation tasks use Sentence-level translation models, but such architectures can’t effectively capture contextual relationships, resulting in the inability to express cross-sentence semantics in a correct way. However, a Document-level translation architecture can deal with the above problems. Document-level translation model can be used for the task of dialogue translation, but how to choose a useful context to help the current sentence to be translated is still an open issue. In addition, there are fewer studies focusing on the influence of historical chat records on translation effects. The historical information can also effectively help the current sentence to be translated, so we think it should be taken into account. Therefore, we propose a new way of context selection and a model adapted to dialogue translation, called United method and Triple-Unified-Transformer, respectively. Our model can better learn the relationship between sentences, so that the current sentence to be translated can get better results. In experiment 1, we use three different chat translation datasets and auto evaluation metrics to measure the effectiveness of our proposed United method and Triple-Unified-Transformer. The results show that the performance of one of the baseline models after adding the United method can improve the translation effect. On the other hand, the translation effect of Triple-Unified-Transformer achieve good results in specific datasets (BLEU, BLONDE). Furthermore, In experiment 2, we test the United method and Triple-Unified-Transformer are general and can adapt to different chat situations. The results show that Triple-Unified-Transformer performs best in a specific dataset, which means that our model has the ability to apply in different chat situations, and the baseline model with the United method can perform better.
顯示於類別:	[資訊管理研究所] 博碩士論文

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