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Multimodal machine translation (MMT) aims to improve translation quality by equipping the source sentence with its corresponding image.
Distilling translations with visual awareness
Julia Ive, Pranava Swaroop Madhyastha, and Lucia Specia. 2019 · 1906
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A mutual information maximization perspective of language representation learning
Lingpeng Kong, Cyprien de Masson d’Autume, Wang Ling, Lei Yu, Zihang Dai, and Dani Yogatama. 2020 · 1910
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Meteor universal: Language specific translation evaluation for any target language
Michael J. Denkowski and Alon Lavie. 2014 · 2014
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Multi30k: Multilingual english-german image descriptions
Desmond Elliott, Stella Frank, K. Sima’an, and Lucia Specia. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Incorporating global visual features into attention-based neural machine translation
Iacer Calixto and Qun Liu. 2017 · 2017
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Doubly-attentive decoder for multi-modal neural machine translation
Iacer Calixto, Qun Liu, and Nick Campbell. 2017 · 2017
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An empirical study on the effectiveness of images in multimodal neural machine translation
Jean-Benoit Delbrouck and Stéphane Dupont. 2017 · 2017
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Imagination improves multimodal translation
Desmond Elliott and Ákos Kádár. 2017 · 2017
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Representation of linguistic form and function in recurrent neural networks
Ákos Kádár, Grzegorz Chrupała, and A. Alishahi. 2017 · 2017
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Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Adversarial evaluation of multimodal machine translation
Desmond Elliott. 2018 · 2018
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The memad submission to the wmt18 multimodal translation task
Stig-Arne Grönroos, Benoit Huet, Mikko Kurimo, Jorma T. Laaksonen, Bernard Mérialdo, Phu Pham, Mats Sjöberg, Umut Sulubacak, Jörg Tiedemann, Raphael Troncy, and Raúl Vázquez. 2018 · 2018
Cited alongside, same era.
Cuni system for the wmt18 multimodal translation task
Jindřich Helcl and Jindřich Libovický. 2018 · 2018
Cited alongside, same era.
Large margin neural language model
Jiaji Huang, Yi Li, Wei Ping, and Liang Huang. 2018 · 2018
Cited alongside, same era.
An efficient framework for learning sentence representations
Lajanugen Logeswaran and Honglak Lee. 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Cited alongside, same era.
Measuring and increasing context usage in context-aware machine translation
Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins. 2021 · 2021
Later among the works it cites.
Contrastive learning for context-aware neural machine translation using coreference information
Yong keun Hwang, Hyungu Yun, and Kyomin Jung. 2021 · 2021
Later among the works it cites.
Vision matters when it should: Sanity checking multimodal machine translation models
Jiaoda Li, Duygu Ataman, and Rico Sennrich. 2021 · 2021
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Gumbel-attention for multi-modal machine translation
Peng Liu, Hailong Cao, and Tiejun Zhao. 2021 · 2021
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Contrastive learning for many-to-many multilingual neural machine translation
Xiao Pan, Mingxuan Wang, Liwei Wu, and Lei Li. 2021 · 2021
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Latent variable model for multi-modal translation
Iacer Calixto, Miguel Rios, and W. Aziz. 2019 · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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It’s easier to translate out of english than into it: Measuring neural translation difficulty by cross-mutual information
Emanuele Bugliarello, Sabrina J. Mielke, Antonios Anastasopoulos, Ryan Cotterell, and Naoaki Okazaki. 2020 · 2020
Cited alongside, same era.
Dynamic context-guided capsule network for multimodal machine translation
Huan Lin, Fandong Meng, Jinsong Su, Yongjing Yin, Zhengyuan Yang, Yubin Ge, Jie Zhou, and Jiebo Luo. 2020 · 2020
Cited alongside, same era.
Multimodal transformer for multimodal machine translation
Shaowei Yao and Xiaojun Wan. 2020 · 2020
Cited alongside, same era.
A novel graph-based multi-modal fusion encoder for neural machine translation
Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, and Jiebo Luo. 2020 · 2020
Cited alongside, same era.
Neural machine translation with universal visual representation
Zhuosheng Zhang, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Z. Li, and Hai Zhao. 2020 · 2020
Cited alongside, same era.
Product-oriented machine translation with cross-modal cross-lingual pre-training
Yuqing Song, Shizhe Chen, Qin Jin, Wei Luo, Jun Xie, and Fei Huang. 2021 · 2021
Later among the works it cites.
Efficient object-level visual context modeling for multimodal machine translation: Masking irrelevant objects helps grounding
Dexin Wang and Deyi Xiong. 2021 · 2021
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Zhiyong Wu, Lingpeng Kong, Wei Bi, Xiang Li, and Benjamin C.M. Kao. 2021 · 2021
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Frequency-aware contrastive learning for neural machine translation
Tong Zhang, Wei Ye, Baosong Yang, Long Zhang, Xingzhang Ren, Dayiheng Liu, Jinan Sun, Shikun Zhang, Haibo Zhang, and Wen Zhao. 2021 · 2021
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Neural machine translation with phrase-level universal visual representations
Qingkai Fang and Yang Feng. 2022 · 2022
Closest in time.
On vision features in multimodal machine translation
Bei Li, Chuanhao Lv, Zefan Zhou, Tao Zhou, Tong Xiao, Anxiang Ma, and Jingbo Zhu. 2022 · 2022
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Xiao Wang, Shihan Dou, Li Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, and Xuanjing Huang. 2022 · 2022
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Region-attentive multimodal neural machine translation
Yuting Zhao, Mamoru Komachi, Tomoyuki Kajiwara, and Chenhui Chu. 2022 · 2022
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