Takip et
Tosho Hirasawa
Tosho Hirasawa
Omron Sinic X
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Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Multimodal machine translation with embedding prediction
T Hirasawa, H Yamagishi, Y Matsumura, M Komachi
arXiv preprint arXiv:1904.00639, 2019
162019
Keyframe segmentation and positional encoding for video-guided machine translation challenge 2020
T Hirasawa, Z Yang, M Komachi, N Okazaki
arXiv preprint arXiv:2006.12799, 2020
142020
Towards multimodal simultaneous neural machine translation
A Imankulova, M Kaneko, T Hirasawa, M Komachi
arXiv preprint arXiv:2004.03180, 2020
142020
TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification?
T Enomoto, H Kim, T Hirasawa, Y Nagai, A Sato, K Nakajima, M Komachi
Proceedings of the 19th Workshop on Innovative Use of NLP for Building …, 2024
82024
Debiasing Word Embeddings Improves Multimodal Machine Translation
T Hirasawa, M Komachi
arXiv preprint arXiv:1905.10464, 2019
82019
Pre-trained word embedding and language model improve multimodal machine translation: A case study in Multi30K
T Hirasawa, M Kaneko, A Imankulova, M Komachi
IEEE Access 10, 67653-67668, 2022
72022
Zero-shot North Korean to English neural machine translation by character tokenization and phoneme decomposition
H Kim, T Hirasawa, M Komachi
Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020
52020
Why videos do not guide translations in video-guided machine translation? An empirical evaluation of video-guided machine translation dataset
Z Yang, T Hirasawa, M Komachi, N Okazaki
Journal of Information Processing 30, 388-396, 2022
42022
Does Masked Language Model Pre-training with Artificial Data Improve Low-resource Neural Machine Translation?
H Tamura, T Hirasawa, H Kim, M Komachi
Findings of the Association for Computational Linguistics: EACL 2023, 2216-2225, 2023
32023
TMU Japanese-English multimodal machine translation system for wat 2020
H Tamura, T Hirasawa, M Kaneko, M Komachi
Proceedings of the 7th workshop on Asian translation, 80-91, 2020
32020
Korean-to-japanese neural machine translation system using hanja information
H Kim, T Hirasawa, M Komachi
Proceedings of the 7th Workshop on Asian Translation, 127-134, 2020
32020
Neural machine translation from historical Japanese to contemporary Japanese using diachronically domain-adapted word embeddings
M Takaku, T Hirasawa, M Komachi, K Komiya
Proceedings of the 34th Pacific Asia Conference on Language, Information and …, 2020
32020
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
D Suzuki, Y Takahashi, I Yamashita, T Aida, T Hirasawa, M Nakatsuji, ...
arXiv preprint arXiv:2201.08038, 2022
22022
English-to-Japanese diverse translation by combining forward and backward outputs
M Kaneko, A Imankulova, T Hirasawa, M Komachi
Proceedings of the Fourth Workshop on Neural Generation and Translation, 134-138, 2020
22020
Visual Prediction Improves Zero-Shot Cross-Modal Machine Translation
T Hirasawa, E Bugliarello, D Elliott, M Komachi
Proceedings of the Eighth Conference on Machine Translation, 522-535, 2023
12023
North korean neural machine translation through south korean resources
H Kim, H Tosho, S Moon, N Okazaki, M Komachi
ACM Transactions on Asian and Low-Resource Language Information Processing …, 2023
12023
Machine Translation with Pre-specified Target-side Words Using a Semi-autoregressive Model
S Kondo, A Koyama, T Kiyuna, T Hirasawa, M Komachi
Proceedings of the 8th Workshop on Asian Translation (WAT2021), 68-73, 2021
12021
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
R Shinoda, K Saito, S Tanaka, T Hirasawa, Y Ushiku
arXiv preprint arXiv:2412.17606, 2024
2024
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
K Maeda, T Hirasawa, A Hashimoto, J Harashima, L Rybicki, Y Fukasawa, ...
European Conference on Computer Vision, 123-140, 2024
2024
Pruning Multilingual Large Language Models for Multilingual Inference
H Kim, J Suzuki, T Hirasawa, M Komachi
arXiv preprint arXiv:2409.16911, 2024
2024
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