Search Results (2)

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity, as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese–Kazakh (Arabic script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively. By combining the generation of pseudo-parallel corpora through phrase replacement with the application of R-Drop regularization, there is a significant advancement in machine translation performance for low-resource languages. Full article

Alley cropping allows the famer to effectively use available resources and yield more benefits. Choosing suitable associated crop and mitigating the competition between trees and crops are crucial for designing the alley cropping systems. We conducted a long-term experiment, including apple (Malus pumila)/peanut (Arachis hypogaea), apple/millet (Setaria italica) and apple/maize (Zea mays) alley cropping systems with conventional intercropping distance, and corresponding monocultures (Exp.1), and a short-term experiment with improved intercropping distance in the same three combinations (Exp.2) in the Loess Plateau, China. The results showed crop yields in three alley cropping systems were lower than the corresponding monocultures. Apple yields were significantly constrained by millet and maize in the alley cropping systems, but not sensitive to the presence of peanut. Land equivalent ratios (LERs) ranged from 0.44 to 0.89 before the tree bore fruit. The LERs were greater than 1.0 after the tree bore fruit, and the apple trees made a decisive contribution to the land use advantage. Net present values of three alley cropping systems were on average 60.1% higher than the corresponding monocultures across the alley cropping period. The maximum annual present value in the first–fifth, sixth and seventh–ninth years after the alley cropping establishment was observed in the apple/maize, apple/millet and apple/peanut system, respectively. These results highlight that choosing the optimal alley cropping management and suitable associated crops at different years after establishment may allow farmers to increase the land use efficiency and economic profitability. Full article