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LIUM-CVC Submissions for WMT17 Multimodal Translation Task

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Résumé

This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two mul-timodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En→De and En→Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.
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Dates et versions

hal-01742382 , version 1 (26-03-2018)

Identifiants

  • HAL Id : hal-01742382 , version 1

Citer

Ozan Caglayan, Walid Aransa, Adrien Bardet, Mercedes Garcia-Martinez, Fethi Bougares, et al.. LIUM-CVC Submissions for WMT17 Multimodal Translation Task. SECOND CONFERENCE ON MACHINE TRANSLATION, 2017, Copenhagen, Denmark. pp.432 - 439. ⟨hal-01742382⟩
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