Comparison of Cross-lingual Strategies for AMR-to-Brazilian Portuguese Generation

Abstract This work presents a study of different strategies for tackling low-resource AMR-to-text generation for Brazilian Portuguese on three approaches. In particular, we explore the use of English AMR as an interlingua and transfer learning (TL). The results suggest that using AMR as an interlingua can be a strong baseline. However, we need to consider the bilingual dictionary used in the concept translation, as it can harm text generation. On the other hand, TL seems to be a promising strategy for generating accurate outputs, but its contribution is not significant. Finally, we present a transformer-based model with the best performance, surpassing all baselines.