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Multimodal Machine Translation
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Multimodal machine translation is the task of doing machine translation with multiple data sources - for example, translating "a bird is flying over water" + an image of a bird over water to German text.
<span style="color:grey; opacity: 0.6">( Image credit: Findings of the Third Shared Task on Multimodal Machine Translation )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ViTA: Visual-Linguistic Translation by Aligning Object Tags
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation
Gumbel-Attention for Multi-modal Machine Translation
MultiSubs: A Large-scale Multimodal and Multilingual Dataset
Cross-lingual Visual Pre-training for Multimodal Machine Translation
Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding
MSVD-Turkish: A Comprehensive Multimodal Dataset for Integrated Vision and Language Research in Turkish
Generative Imagination Elevates Machine Translation
Dynamic Context-guided Capsule Network for Multimodal Machine Translation
Self-Knowledge Distillation with Progressive Refinement of Targets
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach
Towards Automatic Face-to-Face Translation
Multimodal Machine Translation through Visuals and Speech
Adaptive Fusion Techniques for Multimodal Data
Transformer-based Cascaded Multimodal Speech Translation
On Leveraging the Visual Modality for Neural Machine Translation
Probing Representations Learned by Multimodal Recurrent and Transformer Models
Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation
Distilling Translations with Visual Awareness
Image-to-Image Translation with Multi-Path Consistency Regularization
Debiasing Word Embeddings Improves Multimodal Machine Translation
Multimodal Machine Translation with Embedding Prediction
Probing the Need for Visual Context in Multimodal Machine Translation
Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face
Triple consistency loss for pairing distributions in GAN-based face synthesis
Latent Variable Model for Multi-modal Translation
UMONS Submission for WMT18 Multimodal Translation Task
Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report
The MeMAD Submission to the WMT18 Multimodal Translation Task
A Visual Attention Grounding Neural Model for Multimodal Machine Translation
Recycle-GAN: Unsupervised Video Retargeting
Doubly Attentive Transformer Machine Translation
Multimodal Machine Translation with Reinforcement Learning
Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
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