Xugang Lu


Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training
Haipeng Sun | Rui Wang | Kehai Chen | Xugang Lu | Masao Utiyama | Eiichiro Sumita | Tiejun Zhao
Proceedings of the 28th International Conference on Computational Linguistics

Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community. The main advantage of the UNMT lies in its easy collection of required large training text sentences while with only a slightly worse performance than supervised neural machine translation which requires expensive annotated translation pairs on some translation tasks. In most studies, the UMNT is trained with clean data without considering its robustness to the noisy data. However, in real-world scenarios, there usually exists noise in the collected input sentences which degrades the performance of the translation system since the UNMT is sensitive to the small perturbations of the input sentences. In this paper, we first time explicitly take the noisy data into consideration to improve the robustness of the UNMT based systems. First of all, we clearly defined two types of noises in training sentences, i.e., word noise and word order noise, and empirically investigate its effect in the UNMT, then we propose adversarial training methods with denoising process in the UNMT. Experimental results on several language pairs show that our proposed methods substantially improved the robustness of the conventional UNMT systems in noisy scenarios.


The NICT ASR system for IWSLT 2013
Chien-Lin Huang | Paul R. Dixon | Shigeki Matsuda | Youzheng Wu | Xugang Lu | Masahiro Saiko | Chiori Hori
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This study presents the NICT automatic speech recognition (ASR) system submitted for the IWSLT 2013 ASR evaluation. We apply two types of acoustic features and three types of acoustic models to the NICT ASR system. Our system is comprised of six subsystems with different acoustic features and models. This study reports the individual results and fusion of systems and highlights the improvements made by our proposed methods that include the automatic segmentation of audio data, language model adaptation, speaker adaptive training of deep neural network models, and the NICT SprinTra decoder. Our experimental results indicated that our proposed methods offer good performance improvements on lecture speech recognition tasks. Our results denoted a 13.5% word error rate on the IWSLT 2013 ASR English test data set.


Factored Language Model based on Recurrent Neural Network
Youzheng Wu | Xugang Lu | Hitoshi Yamamoto | Shigeki Matsuda | Chiori Hori | Hideki Kashioka
Proceedings of COLING 2012

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The NICT ASR system for IWSLT2012
Hitoshi Yamamoto | Youzheng Wu | Chien-Lin Huang | Xugang Lu | Paul R. Dixon | Shigeki Matsuda | Chiori Hori | Hideki Kashioka
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our automatic speech recognition (ASR) system for the IWSLT 2012 evaluation campaign. The target data of the campaign is selected from the TED talks, a collection of public speeches on a variety of topics spoken in English. Our ASR system is based on weighted finite-state transducers and exploits an combination of acoustic models for spontaneous speech, language models based on n-gram and factored recurrent neural network trained with effectively selected corpora, and unsupervised topic adaptation framework utilizing ASR results. Accordingly, the system achieved 10.6% and 12.0% word error rate for the tst2011 and tst2012 evaluation set, respectively.

Factored recurrent neural network language model in TED lecture transcription
Youzheng Wu | Hitoshi Yamamoto | Xugang Lu | Shigeki Matsuda | Chiori Hori | Hideki Kashioka
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

In this study, we extend recurrent neural network-based language models (RNNLMs) by explicitly integrating morphological and syntactic factors (or features). Our proposed RNNLM is called a factored RNNLM that is expected to enhance RNNLMs. A number of experiments are carried out on top of state-of-the-art LVCSR system that show the factored RNNLM improves the performance measured by perplexity and word error rate. In the IWSLT TED test data sets, absolute word error rate reductions over RNNLM and n-gram LM are 0.4∼0.8 points.