Abstract
In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT.
Highlights
Machine learning model based on deep neural network (DNN) has achieved great breakthrough in many application fields
As a part of natural language processing (NLP), application of deep learning on machine translation (MT) can be divided into two types: Neural Machine Translation (NMT) and deep learning applied on phrase-based machine translation (PBMT) [5, 6]
PBMT which is called traditional machine translation now is facing the impact of NMT, which is a new neural-network-based model of MT
Summary
Machine learning model based on deep neural network (DNN) has achieved great breakthrough in many application fields. It is currently becoming a dominant method in both image recognition and automatic speech recognition [1]. To the best of our knowledge, the idea of DNN has not achieved comparable success in NLP. This is due to the fact that, unlike image or voice, structure of the language is more complex and feature extraction is more difficult. PBMT which is called traditional machine translation now is facing the impact of NMT, which is a new neural-network-based model of MT. With the good translation performance and simple structure, NMT draws most attentions on application of neural network on the MT
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
