Issue #93 – Semantic Neural Machine Translation using AMR
06 Aug20
Issue #93 – Semantic Neural Machine Translation using AMR
Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic
Introduction
Semantic representations were part of the very early Machine Translation (MT) systems, yet have had little role in recent Neural MT (NMT) systems. Given that a good translation should reflect the meaning of the source text, this seems an important area to focus on, particularly since the abstraction could potentially help handle data sparsity. In today’s blog post, we look at work by Song et al (2019) which is a first attempt to incorporate Abstract Meaning Representation (AMR) as a semantic representation for NMT, and significantly improves on the baseline.
The only previous attempt to incorporate semantics in NMT has involved Semantic Role Labelling (Marcheggiani et al. (2018)), which incorporates a shallow representation in the form of arguments and predicates. This in itself helps resolve issues with translating passive/active voice. AMR goes further: it encapsulates the meaning of a sentence as a directed graph, where the nodes are concepts (such as give-01 and John below) and the edges are the relations between the concepts. As can be seen from the