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Modular Neural Machine Translation with a Semantic Pivot - Pilot Study Using AMR

Neural machine translation (NMT) has become the predominant approach for automated translation, yet conventional models trained on extensive bilingual datasets exhibit critical limitations, including quadratic scaling of training data, sensitivity to out-of-distribution inputs, and a lack of interpretability. Inspired by the classical "translation pyramid" concept, which advocates for translation via a semantic pivot (interlingua), this work explores the integration of Abstract Meaning Representation (AMR) as a structured semantic intermediary to decouple translation into comprehension (source-to-AMR) and generation (AMR-to-target) phases. We conduct a pilot study using a strong AMR parser to create a multilingual silver-standard AMR corpus from the United Nations Parallel Corpus, training modular semantic understanding and generation components for each language. Experimental results demonstrate that our approach achieves an average improvement of 3% in robustness and over 15% in generalization compared to traditional Seq2Seq baselines. Analysis suggests that enhancing semantic parsing and generation accuracy could bridge the gap to conventional NMT systems. To our knowledge, this is the first work to integrate AMR as a semantic pivot in NMT, offering enhanced transparency, scalability, and robustness. This study underscores the potential of semantic-driven translation frameworks and provides a foundation for future research in interpretable, resource-efficient multilingual systems.