LIUM-CVC Submissions for WMT17 Multimodal Translation Task

Autor:	Caglayan, Ozan, Aransa, Walid, Bardet, Adrien, García-Martínez, Mercedes, Bougares, Fethi, Barrault, Loïc, Masana, Marc, Herranz, Luis, van de Weijer, Joost
Rok vydání:	2017
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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 multimodal 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. Comment: MMT System Description Paper for WMT17
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.04481 Zobrazit plný text záznamu View this record from Arxiv