Transfer learning based on lexical constraint mechanism in low-resource machine translation

Abstract

With the fast development of artificial intelligence (AI) technology, machine translation has become a mainstream field of natural language processing. The low-resource language machine translation tasks have become an essential question. However, traditional machine translation systems usually rely on large amounts of high-quality parallel training data. In terms of this question, data augmentation and transfer learning technique in AI domain have become an effective solution for dealing with low-resource language machine translation. Besides, to better solve the domain mismatch problem of machine translation tasks, leveraging lexical constraint mechanism is a significant measure. We presented an approach which applies the transfer learning techniques for the lexical constraint model in this paper. For the existed problem of the transfer learning and lexical constraint technologies, some improved methods are proposed. We choose the appropriate beam search algorithm for lexical constraint measure and investigate the proper way for transferring parameters across two machine translation models. Besides, we will also investigate the compelling data pre-processing steps to process the low-resource corpus and quote various objective evaluation mechanisms to estimate the performance of our pattern better. The comprehensive experiments and results in the paper demonstrate that our method toward low-resource machine translation tasks is effective.