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Recently, neural models led to significant improvements in both machinetranslation (MT) and natural language generation tasks (NLG). However,generation of long descriptive summaries conditioned on structured data remainsan open challenge. Likewise, MT that goes beyond sentence-level context isstill an open issue (e.g., document-level MT or MT with metadata). To addressthese challenges, we propose to leverage data from both tasks and do transferlearning between MT, NLG, and MT with source-side metadata (MT+NLG). First, wetrain document-based MT systems with large amounts of parallel data. Then, weadapt these models to pure NLG and MT+NLG tasks by fine-tuning with smalleramounts of domain-specific data. This end-to-end NLG approach, without dataselection and planning, outperforms the previous state of the art on theRotowire NLG task. We participated to the "Document Generation and Translation"task at WNGT 2019, and ranked first in all tracks.

Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019