arXiv:1606.07481
CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks
원문 초록
Neural sequence to sequence learning recently became a very promising paradigm in machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.
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CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Multimodal Machine Translation with Visual Scene Graph Pruning
TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
Gender Bias in English-to-Greek Machine Translation
토론
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