# Semantic Neural Machine Translation using AMR

## TL;DR

This paper explores the integration of Abstract Meaning Representation (AMR) into neural machine translation to enhance meaning preservation and address data sparsity, demonstrating significant improvements in English-German translation.

## Contribution

It introduces a novel method of incorporating AMR into neural machine translation, which has been underexplored in prior work.

## Key findings

- AMR integration improves translation quality
- Significant gains over baseline models
- Effective use of semantic information in NMT

## Abstract

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.