Year: 2025
Type: article
Abstract: Deep learning has transformed Neural Machine Translation (NMT), but the complexity of these models makes them hard to interpret, thereby limiting improvements in translation quality. This study explores the widely used Transformer model, utilizing linguistic features to clarify its inner workings. By incorporating three linguistic features—part-of-speech, dependency relations, and syntax trees—we demonstrate how the model's attention mechanism interacts with these features during translation. Additionally, we improved translation quality by masking nodes that were identified to have negative effects. Our approach bridges the complex nature of NMT with clear linguistic knowledge, offering a more intuitive understanding of the model's translation process.
Authors: Guanghui CAI, Junguo ZHU
Institution: Kunming University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Award: G7249398311
OpenAlex ID: W4410353246