Zhongjun He

Semi-Supervised Learning for Neural Machine Translation

2016 · arXiv (Cornell University)

While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource …

Improved Neural Machine Translation with SMT Features

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system …

Minimum Risk Training for Neural Machine Translation

2016

We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily …

Robust Neural Machine Translation with Joint Textual and Phonetic Embedding

2019

Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. …

End-to-End Speech Translation with Knowledge Distillation

2019

End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years.Compared to conventional pipepine systems, end-to-end ST models have advantages of lower latency, smaller …