Multimodal machine translation through visuals and speech

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-09-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
51
97-147
Series
MACHINE TRANSLATION, Volume 34, issue 2-3
Abstract
Multimodal machine translation involves drawing information from more than one modality, based on the assumption that the additional modalities will contain useful alternative views of the input data. The most prominent tasks in this area are spoken language translation, image-guided translation, and video-guided translation, which exploit audio and visual modalities, respectively. These tasks are distinguished from their monolingual counterparts of speech recognition, image captioning, and video captioning by the requirement of models to generate outputs in a different language. This survey reviews the major data resources for these tasks, the evaluation campaigns concentrated around them, the state of the art in end-to-end and pipeline approaches, and also the challenges in performance evaluation. The paper concludes with a discussion of directions for future research in these areas: the need for more expansive and challenging datasets, for targeted evaluations of model performance, and for multimodality inboth the input and output space.
Description
| openaire: EC/H2020/780069/EU//MeMAD
Keywords
Image-guided translation, Machine translation, Multimodal machine translation, Natural language processing, Speech language translation
Other note
Citation
Sulubacak , U , Caglayan , O , Grönroos , S A , Rouhe , A , Elliott , D , Specia , L & Tiedemann , J 2020 , ' Multimodal machine translation through visuals and speech ' , MACHINE TRANSLATION , vol. 34 , no. 2-3 , pp. 97-147 . https://doi.org/10.1007/s10590-020-09250-0