Classifying Divergences in Cross-lingual AMR Pairs
Shira Wein, Nathan Schneider · 2021
Translation divergences are varied and widespread, challenging approaches that rely on parallel text.To annotate translation divergences, we propose a schema grounded in the Abstract Meaning Representation (AMR), a sentence-level semantic framework instantiated for a number of languages.By comparing parallel AMR graphs, we can identify specific points of divergence.Each divergence is labeled with both a type and a cause.We release a small corpus of annotated English-Spanish data, and analyze the annotations in our corpus.