Muhammad Naeem Ul Hassan1,2, Zhengtao Yu1,2,*, Jian Wang1,2, Ying Li1,2, Shengxiang Gao1,2, Shuwan Yang1,2, Cunli Mao1,2
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 951-969, 2024, DOI:10.32604/cmc.2024.054673
- 15 October 2024
Abstract Thanks to the strong representation capability of pre-trained language models, supervised machine translation models have achieved outstanding performance. However, the performances of these models drop sharply when the scale of the parallel training corpus is limited. Considering the pre-trained language model has a strong ability for monolingual representation, it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models. To alleviate the dependence on the parallel corpus, we propose a Linguistics Knowledge-Driven Multi-Task (LKMT) approach to… More >
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