Simulation of Machine-Assisted Translation Model Based on Python Data Analysis

Research on machine-assisted translation possesses significant practical implications. Despite considerable advancements in model learning for this field, prevalent model learning techniques still encounter numerous challenges in parameter acquisition and model architecture. This study undertakes a simulation investigation of a machine-assisted translation model grounded in Python data analysis. We introduce a novel approach that seamlessly integrates the source syntax tree into the encoder-decoder paradigm. Our encoder design incorporates a bidirectional GRU (Gated Recurrent Unit) RNN (Recurrent Neural Network), which entails a forward and a reverse RNN. This configuration allows the syntax tree information to be initially processed by the root node and subsequently propagated to its descendants via a top-down encoder. Simulation outcomes reveal that as sentence length surpasses 20 words, the performance enhancement offered by our proposed model becomes increasingly evident. Notably, the most significant gains are observed in sentences exceeding 25 words. The effect of the retrained model quickly restores the baseline of the uncut model, which ensures a very low precision loss. This is undoubtedly a very effective model compression method.