Papers by Ryoya Yuasa
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation (2022.lrec-1)
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Kazuki Tani, Ryoya Yuasa, Kazuki Takikawa, Akihiro Tamura, Tomoyuki Kajiwara, Takashi Ninomiya, Tsuneo Kato
| Challenge: | Existing test datasets for MLCC-MT have three problems: A source language sentence and its simplified target language sentence are not necessarily exactly parallel. |
| Approach: | They propose to use a test dataset to evaluate multi-level complexity-controllable machine translation (MLCC-MT) their results are compared to a standard test dataset constructed from the Newsela corpus . |
| Outcome: | The proposed test dataset is based on the Newsela corpus and is released . it includes automatic filtering, manual check for parallel target language sentences . |
Multimodal Neural Machine Translation Using Synthetic Images Transformed by Latent Diffusion Model (2023.acl-srw)
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| Challenge: | Existing methods to translate source language sentences using images are not optimal for machine translation. |
| Approach: | They propose a new multimodal neural machine translation model using synthetic images transformed by a latent diffusion model. |
| Outcome: | The proposed model improves translation performance on English-German translation tasks using the Multi30k dataset. |