Jen-Tzung Chien

Also published as: Jen-Tzong Chien


2019

pdf bib
Deep Bayesian Natural Language Processing
Jen-Tzung Chien
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

This introductory tutorial addresses the advances in deep Bayesian learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, “deep learning” is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The “semantic structure” in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The “distribution function” in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network, long short-term memory, sequence-to-sequence model, variational auto-encoder, generative adversarial network, attention mechanism, memory-augmented neural network, skip neural network, stochastic neural network, predictive state neural network and policy neural network. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies and domain applications are presented to tackle different issues in deep Bayesian processing, learning and understanding. At last, we will point out a number of directions and outlooks for future studies.

2018

pdf bib
Deep Bayesian Learning and Understanding
Jen-Tzung Chien
Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts

pdf bib
Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018)
Chi-Chun (Jeremy) Lee | Cheng-Zen Yang | Jen-Tzung Chien
Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018)

2017

pdf bib
International Journal of Computational Linguistics & Chinese Language Processing, Volume 22, Number 1, June 2017
Yuen-Hsien Tseng | Jen-Tzung Chien
International Journal of Computational Linguistics & Chinese Language Processing, Volume 22, Number 1, June 2017

2016

pdf bib
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016
Yuen-Hsien Tseng | Jen-Tzung Chien
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016

2015

pdf bib
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)
Sin-Horng Chen | Hsin-Min Wang | Jen-Tzung Chien
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

pdf bib
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015 - Special Issue on Selected Papers from ROCLING XXVII
Hung-Yu Kao | Yih-Ru Wang | Jen-Tzong Chien
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015 - Special Issue on Selected Papers from ROCLING XXVII

2014

pdf bib
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 4, December 2014 - Special Issue on Selected Papers from ROCLING XXVI
Jen-Tzung Chien | Hung-Yu Kao | Chia-Hui Chang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 4, December 2014 - Special Issue on Selected Papers from ROCLING XXVI

2007

pdf bib
貝氏主題混合資訊檢索模型 (Bayesian Topic Mixture Model for Information Retrieval) [In Chinese]
Meng-Sung Wu | Hsuan-Jui Hsu | Jen-Tzung Chien
Proceedings of the 19th Conference on Computational Linguistics and Speech Processing

2006

pdf bib
鑑別性事前資訊應用於強健性語音辨識 (Robust Speech Recognition Using Discriminative Prior Statistics) [In Chinese]
Chuan-Wei Ting | Bo-Shu Wu | Jen-Tzung Chien
Proceedings of the 18th Conference on Computational Linguistics and Speech Processing

pdf bib
A Maximum Entropy Approach for Semantic Language Modeling
Chuang-Hua Chueh | Hsin-Min Wang | Jen-Tzung Chien
International Journal of Computational Linguistics & Chinese Language Processing, Volume 11, Number 1, March 2006: Special Issue on Human Computer Speech Processing

2005

bib
Proceedings of the 17th Conference on Computational Linguistics and Speech Processing
Chung-Hsien Wu | Jen-Tzung Chien | Wen-Hsiang Lu
Proceedings of the 17th Conference on Computational Linguistics and Speech Processing

pdf bib
Perceptual Factor Analysis for Speech Enhancement
Chuan-Wei Ting | Jen-Tzung Chien
Proceedings of the 17th Conference on Computational Linguistics and Speech Processing

pdf bib
TAICAR – The Collection and Annotation of an In-Car Speech Database Created in Taiwan
Hsien-Chang Wang | Chung-Hsien Yang | Jhing-Fa Wang | Chung-Hsien Wu | Jen-Tzung Chien
International Journal of Computational Linguistics & Chinese Language Processing, Volume 10, Number 2, June 2005: Special Issue on Annotated Speech Corpora

2004

pdf bib
聚集事後機率線性迴歸調適演算法應用於語音辨識 (Aggregate a Posteriori Linear Regression for Speech Recognition) [In Chinese]
Chih-Hsien Huang | Yii-Kai Wang | Jen-Tzung Chien
Proceedings of the 16th Conference on Computational Linguistics and Speech Processing

pdf bib
具相關資訊回饋能力之貝氏混合式機率檢索模型 (Using Relevance Feedback in Bayesian Probabilistic Mixture Retrieval Model) [In Chinese]
Jen-Tzung Chien | Duen-Chi Yang
Proceedings of the 16th Conference on Computational Linguistics and Speech Processing

pdf bib
Latent Semantic Language Modeling and Smoothing
Jen-Tzung Chien | Meng-Sung Wu | Hua-Jui Peng
International Journal of Computational Linguistics & Chinese Language Processing, Volume 9, Number 2, August 2004: Special Issue on New Trends of Speech and Language Processing

2001

bib
Proceedings of Research on Computational Linguistics Conference XIV
Chung-Hsien Wu | Jen-Tzung Chien
Proceedings of Research on Computational Linguistics Conference XIV

pdf bib
使用關聯法則為主之語言模型於擷取長距離中文文字關聯性 (Association Rule Based Language Models for Discovering Long Distance Dependency in Chinese) [In Chinese]
Jen-Tzung Chien | Hung-Ying Chen
Proceedings of Research on Computational Linguistics Conference XIV

2000

bib
具有累進學習能力之貝氏預測法則在汽車語音辨識之應用 (Bayesian Predictive Classification with Incremental Learning Capability for Car Speech Recognition) [In Chinese]
Jen-Tzung Chien | Guo-Hong Liao
Proceedings of Research on Computational Linguistics Conference XIII

pdf bib
結合麥克風陣列及模型調整技術之遠距離語音辨識系統 (Far-Distant Speech Recognition System Using Combined Techniques of Microphone Array and Model Adaptation)[In Chinese]
Jain-Ray Lai | Jen-Tzung Chien
Proceedings of Research on Computational Linguistics Conference XIII

1999

pdf bib
音框同步之雜訊補償方法在汽車語音辨識之應用 (Frame Synchronous Noise Compensation for Car Speech Recognition) [In Chinese]
Jen-Tzung Chien | Ming-Shun Lin
Proceedings of Research on Computational Linguistics Conference XII