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Multilingual AMR-to-Text Generation
[article]
2020
arXiv
pre-print
Generating text from structured data is challenging because it requires bridging the gap between (i) structure and natural language (NL) and (ii) semantically underspecified input and fully specified NL output. Multilingual generation brings in an additional challenge: that of generating into languages with varied word order and morphological properties. In this work, we focus on Abstract Meaning Representations (AMRs) as structured input, where previous research has overwhelmingly focused on
arXiv:2011.05443v1
fatcat:qigjt3cqabevnojukorr5itpsi