3 research outputs found
Supervised Attentions for Neural Machine Translation
In this paper, we improve the attention or alignment accuracy of neural
machine translation by utilizing the alignments of training sentence pairs. We
simply compute the distance between the machine attentions and the "true"
alignments, and minimize this cost in the training procedure. Our experiments
on large-scale Chinese-to-English task show that our model improves both
translation and alignment qualities significantly over the large-vocabulary
neural machine translation system, and even beats a state-of-the-art
traditional syntax-based system.Comment: 6 pages. In Proceedings of EMNLP 2016. arXiv admin note: text overlap
with arXiv:1605.0314
Pieces of Eight: 8-bit Neural Machine Translation
Neural machine translation has achieved levels of fluency and adequacy that
would have been surprising a short time ago. Output quality is extremely
relevant for industry purposes, however it is equally important to produce
results in the shortest time possible, mainly for latency-sensitive
applications and to control cloud hosting costs. In this paper we show the
effectiveness of translating with 8-bit quantization for models that have been
trained using 32-bit floating point values. Results show that 8-bit translation
makes a non-negligible impact in terms of speed with no degradation in accuracy
and adequacy.Comment: To appear at NAACL 2018 Industry Trac