A class-specific copy network for handling the rare word problem in neural machine translation

Abstract

Neural machine translation (NMT) has shown promising results and rapidly gained adoption in many large-scale settings. With the NMT model being widely used in empirical productions, its long-standing weakness in handling the rare and out of vocabulary words has been amplified a lot. In order to release the model from the stress of “understanding” the rare words, copy mechanism has been proposed to deal with the rare and unseen words for the neural network models using attention. However the negative side of the copy mechanism is that the model is only able to decide whether to copy or not. It is unable to detect which class should the rare word be copied to, such as person, location, and organization. This paper deeply investigates this limitation of the NMT model. As a result, we propose a new NMT model by novelly incorporating a class-specific copy network. With the network, the proposed NMT model is able to decide which class the words in the target belong to and which class in the source should be copied to. Experimental results on Chinese-English translation tasks show that the proposed model outperforms the traditional NMT model with a large margin especially for sentences containing the rare words.