Abstract
Fahimeh Saleh, Alexandre Berard, Ioan Calapodescu, Laurent Besacier |
Workshop on Neural Generation and Translation (WNGT), EMNLP, Hong Kong, China, 4 November, 2019 |
Abstract
Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation (NLG) tasks.
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 access to metadata. First, we focus on training a document-based MT system with the DGT parallel data. Then, we augment this MT model to obtain a “Data + Text to Text” MT model. Finally, we remove the text part of the input to obtain a pure NLG system, able to translate metadata to full documents. This end-to-end NLG approach, without data selection and planning, outperforms the previous state of the art on the Rotowire NLG dataset.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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