University of Edinburgh's submission to the Document-level Generation and Translation Shared Task

Code

• github.com/ratishsp/data2text-table-plan-py
  Officialpytorch★ 0

Abstract

The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.

Tasks

Reproductions