Subject: Computer Science - Computation and Language | Computer Science - Neural and Evolutionary Computing
Knowing which words have been attended to in previous time steps while generating a translation is a rich source of information for predicting what words will be attended to in the future. We improve upon the attention model of Bahdanau et al. (2014) by explicitly model... View more
[Bahdanau et al.2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
[Chung et al.2016] Junyoung Chung, Kyunghyun Cho, and Yoshua Bengio. 2016. A character-level decoder without explicit segmentation for neural machine translation. arXiv preprint arXiv:1603.06147.
[Cohn et al.2016] Trevor Cohn, Cong Duy Vu Hoang, Ekaterina Vymolova, Kaisheng Yao, Chris Dyer, and Gholamreza Haffari. 2016. Incorporating structural alignment biases into an attentional neural translation model. arXiv preprint arXiv:1601.01085.
[Costa-Jussa` and Fonollosa2016] Marta R Costa-Jussa` and Jose´ AR Fonollosa. 2016. Characterbased neural machine translation. arXiv preprint arXiv:1603.00810.
[Feng et al.2016] Shi Feng, Shujie Liu, Mu Li, and Ming Zhou. 2016. Implicit distortion and fertility models for attention-based encoder-decoder NMT model. CoRR, abs/1601.03317.
[Jean et al.2014] Se´bastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2014. On using very large target vocabulary for neural machine translation. CoRR, abs/1412.2007.
[Kalchbrenner and Blunsom2013] Nal Kalchbrenner and Phil Blunsom. 2013. Recurrent continuous translation models. In Proc. EMNLP.
[Luong et al.2014] Minh-Thang Luong, Ilya Sutskever, Quoc V Le, Oriol Vinyals, and Wojciech Zaremba. 2014. Addressing the rare word problem in neural machine translation. arXiv preprint arXiv:1410.8206.
[Luong et al.2015] Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches