Design of multimodal collaborative representation learning algorithm in English translation tasks
TL;DR
Experiments show that MCRA-Net outperforms baseline models on BLEU, METEOR, Culture-BLEU, and TERM-F1, especially in translating culture-specific and technical terms, especially in translating culture-specific and technical terms.
Abstract
This paper introduces a multimodal collaborative representation learning algorithm (MCRA-Net) to tackle challenges in English translation, including cultural differences, context dependency, terminological accuracy, and polysemy resolution. The algorithm integrates three types of heterogeneous data-text, image and voice-to construct a unified feature representation, thus improving translation accuracy and cultural adaptability. For feature extraction, the text pattern uses BERT for context semantics; the image mode employs ResNet-50 to extract visual cues that help resolve ambiguity and interpret culture-loaded words; the speech mode utilizes Wav2Vec 2.0 to capture intonation, rhythm, and emotional information. These three feature sets are projected into a shared semantic space for cross-modal alignment. A dynamic cross-modal alignment module, based on attention and contrastive learning, strengthens inter-modal associations. A multimodal graph fusion network, built on graph neural networks (GNN), performs deep feature fusion. The decoding stage uses a Transformer decoder optimized with a joint loss function. Experiments on Multi30K EnZh, CultureTerms-2023, and TechDoc MT datasets show that MCRA-Net outperforms baseline models on BLEU, METEOR, Culture-BLEU, and TERM-F1, especially in translating culture-specific and technical terms. Ablation studies confirm the contribution of each module, and the model can dynamically adjust modal weights according to the scenario.
