Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Eriguchi, Akiko"'
Vocabulary adaptation, which integrates new vocabulary into pre-trained language models (LMs), enables expansion to new languages and mitigates token over-fragmentation. However, existing approaches are limited by their reliance on heuristic or exter
Externí odkaz:
http://arxiv.org/abs/2410.09644
Large language models (LLMs) have achieved remarkable success across various NLP tasks, yet their focus has predominantly been on English due to English-centric pre-training and limited multilingual data. While some multilingual LLMs claim to support
Externí odkaz:
http://arxiv.org/abs/2410.03115
Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment costs compared with conventional bilingual systems. The MNMT training be
Externí odkaz:
http://arxiv.org/abs/2206.14982
Multilingual Neural Machine Translation (NMT) enables one model to serve all translation directions, including ones that are unseen during training, i.e. zero-shot translation. Despite being theoretically attractive, current models often produce low
Externí odkaz:
http://arxiv.org/abs/2109.04778
Autor:
Ma, Shuming, Yang, Jian, Huang, Haoyang, Chi, Zewen, Dong, Li, Zhang, Dongdong, Awadalla, Hany Hassan, Muzio, Alexandre, Eriguchi, Akiko, Singhal, Saksham, Song, Xia, Menezes, Arul, Wei, Furu
Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialized Transformer backbone. In this work, inspired by the recent success o
Externí odkaz:
http://arxiv.org/abs/2012.15547
Neural text generation models conditioning on given input (e.g. machine translation and image captioning) are usually trained by maximum likelihood estimation of target text. However, the trained models suffer from various types of errors at inferenc
Externí odkaz:
http://arxiv.org/abs/2012.14124
Autor:
Shen, Jonathan, Nguyen, Patrick, Wu, Yonghui, Chen, Zhifeng, Chen, Mia X., Jia, Ye, Kannan, Anjuli, Sainath, Tara, Cao, Yuan, Chiu, Chung-Cheng, He, Yanzhang, Chorowski, Jan, Hinsu, Smit, Laurenzo, Stella, Qin, James, Firat, Orhan, Macherey, Wolfgang, Gupta, Suyog, Bapna, Ankur, Zhang, Shuyuan, Pang, Ruoming, Weiss, Ron J., Prabhavalkar, Rohit, Liang, Qiao, Jacob, Benoit, Liang, Bowen, Lee, HyoukJoong, Chelba, Ciprian, Jean, Sébastien, Li, Bo, Johnson, Melvin, Anil, Rohan, Tibrewal, Rajat, Liu, Xiaobing, Eriguchi, Akiko, Jaitly, Navdeep, Ari, Naveen, Cherry, Colin, Haghani, Parisa, Good, Otavio, Cheng, Youlong, Alvarez, Raziel, Caswell, Isaac, Hsu, Wei-Ning, Yang, Zongheng, Wang, Kuan-Chieh, Gonina, Ekaterina, Tomanek, Katrin, Vanik, Ben, Wu, Zelin, Jones, Llion, Schuster, Mike, Huang, Yanping, Chen, Dehao, Irie, Kazuki, Foster, George, Richardson, John, Macherey, Klaus, Bruguier, Antoine, Zen, Heiga, Raffel, Colin, Kumar, Shankar, Rao, Kanishka, Rybach, David, Murray, Matthew, Peddinti, Vijayaditya, Krikun, Maxim, Bacchiani, Michiel A. U., Jablin, Thomas B., Suderman, Rob, Williams, Ian, Lee, Benjamin, Bhatia, Deepti, Carlson, Justin, Yavuz, Semih, Zhang, Yu, McGraw, Ian, Galkin, Max, Ge, Qi, Pundak, Golan, Whipkey, Chad, Wang, Todd, Alon, Uri, Lepikhin, Dmitry, Tian, Ye, Sabour, Sara, Chan, William, Toshniwal, Shubham, Liao, Baohua, Nirschl, Michael, Rondon, Pat
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily ex
Externí odkaz:
http://arxiv.org/abs/1902.08295
Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enab
Externí odkaz:
http://arxiv.org/abs/1809.04686
Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduce the first RC system for languages without RC training data. Given a
Externí odkaz:
http://arxiv.org/abs/1809.03275
There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model
Externí odkaz:
http://arxiv.org/abs/1702.03525