Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks, very large data and many training iterations are necessary to achieve state-of-the-art performance for NMT. This results in very high computation cost and slow down research and industrialization. In this paper, we first investigate the instability by randomizations for NMT training, and further...

The vanilla attention-based neural machine translation has achieved promising performance because of its capability in leveraging varying-length source annotations. However, this model still suffers from failures in long sentence translation, for its incapability in capturing long-term dependencies. In this paper, we propose a novel recurrent neural machine translation (RNMT), which not only pr...

Models of neural machine translation are often from a discriminative family of encoder-decoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoder-decoder model that can be trained end-to-end. Different from the vanilla encod...

We present a new approach for neural machine translation (NMT) using the morphological and grammatical decomposition of the words (factors) in the output side of the neural network. This architecture addresses two main problems occurring in MT, namely dealing with a large target language vocabulary and the out of vocabulary (OOV) words. By the means of factors, we are able to handle larger voca...

Named entity translation is indispensable in cross language information retrieval nowadays. We propose an approach of combining lexical information, web statistics, and inverse search based on Google to backward translate a Chinese named entity (NE) into English. Our system achieves a high Top-1 accuracy of 87.6%, which is a relatively good performance reported in this area until present.

We explore the application of neural language models to machine translation. We develop a new model that combines the neural probabilistic language model of Bengio et al., rectified linear units, and noise-contrastive estimation, and we incorporate it into a machine translation system both by reranking k-best lists and by direct integration into the decoder. Our large-scale, large-vocabulary ex...

This paper describes the submission of Johns Hopkins University for the shared translation task of ACL 2016 First Conference on Machine Translation (WMT 2016). We set up phrase-based, hierarchical phrase-based and syntax-based systems for all 12 language pairs of this year’s evaluation campaign. Novel research directions we investigated include: neural probabilistic language models, bilingual n...

In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach in which the information shared among languages can be helpful in the translation of individual language pairs. We are then able to employ attention-based Neural Machine Translation for many-to-many multilingual translation tasks. Our approach does not require an...

Financial decisions are among the most significant life-changing decisions that individuals make. There is a strong correlation between financial decision making and human behavior. In this research the relationship between what people think and how stock market moves is investigated. The data from 2010 to 2015 of some of business, political and financial events which directly impact the local ...