1 research outputs found
Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019
Recently, neural models led to significant improvements in both machine
translation (MT) and natural language generation tasks (NLG). However,
generation of long descriptive summaries conditioned on structured data remains
an open challenge. Likewise, MT that goes beyond sentence-level context is
still an open issue (e.g., document-level MT or MT with metadata). To address
these challenges, we propose to leverage data from both tasks and do transfer
learning between MT, NLG, and MT with source-side metadata (MT+NLG). First, we
train document-based MT systems with large amounts of parallel data. Then, we
adapt these models to pure NLG and MT+NLG tasks by fine-tuning with smaller
amounts of domain-specific data. This end-to-end NLG approach, without data
selection and planning, outperforms the previous state of the art on the
Rotowire NLG task. We participated to the "Document Generation and Translation"
task at WNGT 2019, and ranked first in all tracks.Comment: WNGT 2019 - System Description Pape