Semantic Neural Machine Translation using AMR
This work addresses the challenge of meaning preservation and data sparsity in neural machine translation for researchers and practitioners, though it is incremental as it builds on existing attention-based models.
The authors tackled the problem of improving neural machine translation by incorporating semantic representations, specifically AMR, to enhance meaning preservation and address data sparsity, resulting in significant improvements over a strong baseline model on an English-to-German dataset.
It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (short for abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.