A pseudo-dynamic smoothing approach for low-resource neural machine translation using prompts

In the field of neural machine translation (NMT), improving translation quality remains a significant challenge, especially for low-resource language pairs. Inspired by the prompt-based method, we propose a novel approach called prompt-enhanced pseudo-dynamic smoothing (PEPDS). This method leverages prompts to optimize the attention mechanism and enhance the model’s learning capacity during the training process. Our approach provides additional contextual information through the use of prompts and enhances the model’s ability to handle long-distance dependencies and complex grammatical structures. The PEPDS framework utilize linguistic insights to create effective contextual cues. We focus on four parts of speech in English sentences: nouns, adjectives, adverbs and verbs, to generate prompts that enrich the contextual information available to the model. This method can be viewed as a form of inside knowledge enhancement, particularly beneficial for low-resource scenarios and long sentences. We apply the prompt method during the inference phase, further enhancing the model’s translation performance. We evaluate PEPDS across multiple language pairs, demonstrating significant improvements in translation quality, especially for low-resource language pairs and complex sentences. Experimental results, attention pattern studies and translation case studies all demonstrate the effectiveness of our method.