default search action
14th AMTA 2020: Virtual
- Michael J. Denkowski, Christian Federmann:
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas, AMTA 2020, Virtual, October 6-9, 2020. Association for Machine Translation in the Americas 2020 - Benyamin Ahmadnia, Bonnie J. Dorr:
A New Approach to Parameter-Sharing in Multilingual Neural Machine Translation. 1-6 - Parnia Bahar, Nikita Makarov, Hermann Ney:
Investigation of Transformer-based Latent Attention Models for Neural Machine Translation. 7-20 - Jan Niehues:
Machine Translation with Unsupervised Length-Constraints. 21-35 - Guodong Xie, Andy Way:
Constraining the Transformer NMT Model with Heuristic Grid Beam Search. 36-49 - Jason Naradowsky, Xuan Zhang, Kevin Duh:
Machine Translation System Selection from Bandit Feedback. 50-63 - Anh Khoa Ngo Ho, François Yvon:
Generative latent neural models for automatic word alignment. 64-77 - Alberto Poncelas, Pintu Lohar, James Hadley, Andy Way:
The Impact of Indirect Machine Translation on Sentiment Classification. 78-88 - Viktor Hangya, Alexander M. Fraser:
Towards Handling Compositionality in Low-Resource Bilingual Word Induction. 89-101 - Guillaume Klein, François Hernandez, Vincent Nguyen, Jean Senellart:
The OpenNMT Neural Machine Translation Toolkit: 2020 Edition. 102-109 - Tobias Domhan, Michael J. Denkowski, David Vilar, Xing Niu, Felix Hieber, Kenneth Heafield:
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020. 110-115 - Zhixing Tan, Jiacheng Zhang, Xuancheng Huang, Gang Chen, Shuo Wang, Maosong Sun, Huanbo Luan, Yang Liu:
THUMT: An Open-Source Toolkit for Neural Machine Translation. 116-122 - Yuekun Yao, Barry Haddow:
Dynamic Masking for Improved Stability in Online Spoken Language Translation. 123-136 - Mattia Antonino Di Gangi, Marco Gaido, Matteo Negri, Marco Turchi:
On Target Segmentation for Direct Speech Translation. 137-150 - Mathias Müller, Annette Rios, Rico Sennrich:
Domain Robustness in Neural Machine Translation. 151-164 - Ngoc Tan Le, Fatiha Sadat:
Low-Resource NMT: an Empirical Study on the Effect of Rich Morphological Word Segmentation on Inuktitut. 165-172
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.