文章基本信息

标题：NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning
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作者：Álvaro Peris ; Francisco Casacuberta
期刊名称：The Prague Bulletin of Mathematical Linguistics
印刷版ISSN：0032-6585
电子版ISSN：1804-0462
出版年度：2018
卷号：111
期号：1
页码：113-124
DOI：10.2478/pralin-2018-0010
语种：English
出版社：Walter de Gruyter GmbH
摘要：We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning. NMT-Keras is based on an extended version of the popular Keras library, and it runs on Theano and TensorFlow. State-of-the-art neural machine translation models are deployed and used following the high-level framework provided by Keras. Given its high modularity and flexibility, it also has been extended to tackle different problems, such as image and video captioning, sentence classification and visual question answering.