论文信息 - Why Neural Translations are the Right Length

Why Neural Translations are the Right Length

We investigate how neural, encoder-decoder translation systems output target strings of appropriate lengths, finding that a collection of hidden units learns to explicitly implement this functionality.

[1] Paul J. Werbos,et al. Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[2] Jürgen Schmidhuber,et al. LSTM can Solve Hard Long Time Lag Problems , 1996, NIPS.

[3] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[4] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[5] Franz Josef Och,et al. Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[6] Mikel L. Forcada,et al. Asynchronous translations with recurrent neural nets , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[7] Christopher D. Manning,et al. Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[8] Francisco Casacuberta,et al. A connectionist approach to machine translation , 1997, EUROSPEECH.

[9] Robert L. Mercer,et al. The Mathematics of Statistical Machine Translation: Parameter Estimation , 1993, CL.

[10] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.