Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ji, Baijun"'
Autor:
Wu, Kaixin, Ji, Yixin, Chen, Zeyuan, Wang, Qiang, Wang, Cunxiang, Liu, Hong, Ji, Baijun, Xu, Jia, Liu, Zhongyi, Gu, Jinjie, Zhou, Yuan, Mo, Linjian
Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements of large language models (LLMs) in various natural language processing
Externí odkaz:
http://arxiv.org/abs/2412.01269
Multimodal machine translation (MMT) aims to improve translation quality by equipping the source sentence with its corresponding image. Despite the promising performance, MMT models still suffer the problem of input degradation: models focus more on
Externí odkaz:
http://arxiv.org/abs/2210.08478
Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces. Two classes of word representations have been
Externí odkaz:
http://arxiv.org/abs/2106.03084
Autor:
Duan, Xiangyu, Ji, Baijun, Jia, Hao, Tan, Min, Zhang, Min, Chen, Boxing, Luo, Weihua, Zhang, Yue
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the ability of a monolingual speaker learning to translate via looking up the
Externí odkaz:
http://arxiv.org/abs/2007.02671
Transfer learning between different language pairs has shown its effectiveness for Neural Machine Translation (NMT) in low-resource scenario. However, existing transfer methods involving a common target language are far from success in the extreme sc
Externí odkaz:
http://arxiv.org/abs/1912.01214