关键词: 蒙汉翻译, 多任务学习, Transformer, ELMo, FastText

Abstract:

Mongolian-Chinese translation is a translation of low-resource language, facing the difficulty of the scarcity of parallel corpus resources. In order to alleviate the problem of low translation accuracy caused by the scarcity of parallel corpus data and vocabulary limitation, this paper uses dynamic data pre-training method ELMo（Embeddings from Language Models）, and combines the Transformer translation architecture for multi-tasking domain information sharing in the Mongolian-Chinese translation. Firstly, ELMo（deep contextualized word representation） is used for the pre-training of the Monolingual corpus. Secondly, this paper uses the FastText word embedding algorithm to pre-train the context-related large-scale text in the Mongolian-Chinese parallel corpus. Then, according to the principle of multi-task sharing parameters to realize domain information sharing, a one-to-many encoder-decoder model is constructed for Mongolian-Chinese neural machine translation. The experimental results show that the translation method can effectively improve the translation quality in the long sentence input sequence than the Transformer baseline translation method.

Key words: Mongolian-Chinese translation, multi-task learning, Transformer, ELMo, FastText