Kuan-Yu Chen


2021

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2020福爾摩沙臺語語音辨識比賽之初步實驗 (A Preliminary Study of Formosa Speech Recognition Challenge 2020 – Taiwanese ASR)
Fu-Hao Yu | Ke-Han Lu | Yi-Wei Wang | Wei-Zhe Chang | Wei-Kai Huang | Kuan-Yu Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 26, Number 1, June 2021

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Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
Lung-Hao Lee | Chia-Hui Chang | Kuan-Yu Chen
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

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A Flexible and Extensible Framework for Multiple Answer Modes Question Answering
Cheng-Chung Fan | Chia-Chih Kuo | Shang-Bao Luo | Pei-Jun Liao | Kuang-Yu Chang | Chiao-Wei Hsu | Meng-Tse Wu | Shih-Hong Tsai | Tzu-Man Wu | Aleksandra Smolka | Chao-Chun Liang | Hsin-Min Wang | Kuan-Yu Chen | Yu Tsao | Keh-Yih Su
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4%.

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A BERT-based Siamese-structured Retrieval Model
Hung-Yun Chiang | Kuan-Yu Chen
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Due to the development of deep learning, the natural language processing tasks have made great progresses by leveraging the bidirectional encoder representations from Transformers (BERT). The goal of information retrieval is to search the most relevant results for the user’s query from a large set of documents. Although BERT-based retrieval models have shown excellent results in many studies, these models usually suffer from the need for large amounts of computations and/or additional storage spaces. In view of the flaws, a BERT-based Siamese-structured retrieval model (BESS) is proposed in this paper. BESS not only inherits the merits of pre-trained language models, but also can generate extra information to compensate the original query automatically. Besides, the reinforcement learning strategy is introduced to make the model more robust. Accordingly, we evaluate BESS on three public-available corpora, and the experimental results demonstrate the efficiency of the proposed retrieval model.

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ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models
Yi-Wei Wang | Wei-Zhe Chang | Bo-Han Fang | Yi-Chia Chen | Wei-Kai Huang | Kuan-Yu Chen
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

This technical report aims at the ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In order to predict the affective states of Chinese educational texts, we present a practical framework by employing pre-trained language models, such as BERT and MacBERT. Several valuable observations and analyses can be drawn from a series of experiments. From the results, we find that MacBERT-based methods can deliver better results than BERT-based methods on the verification set. Therefore, we average the prediction results of several models obtained using different settings as the final output.

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ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information
Ke-Han Lu | Kuan-Yu Chen
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

In this paper, we proposed a BERT-based dimensional semantic analyzer, which is designed by incorporating with word-level information. Our model achieved three of the best results in four metrics on “ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts”. We conducted a series of experiments to compare the effectiveness of different pre-trained methods. Besides, the results also proofed that our method can significantly improve the performances than classic methods. Based on the experiments, we also discussed the impact of model architectures and datasets.

2020

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International Journal of Computational Linguistics & Chinese Language Processing, Volume 25, Number 2, December 2020
Lung-Hao Lee | Kuan-Yu Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 25, Number 2, December 2020

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A Preliminary Study on Using Meta-learning Technique for Information Retrieval
Chong-En Lin | Kuan-Yu Chen
Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020)

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A Preliminary Study on Leveraging Meta Learning Technique for Code-switching Speech Recognition
Fu-Hao Yu | Kuan-Yu Chen
Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020)

2019

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基於特徵粒度之訓練策略於中文口語問答系統之應用(A Feature-granularity Training Strategy for Chinese Spoken Question Answering)
Shang-Bao Luo | Kuan-Yu Chen
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)

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新穎的序列生成架構於中文重寫式摘要之研究(Novel Sequence Generation Framework for Chinese Abstractive Summarization)
Chin-Yueh Chien | Kuan-Yu Chen
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)

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EBSUM: 基於BERT 的強健性抽取式摘要法(EBSUM: An Enhanced BERT-based Extractive Summarization Framework)
Zheng-Yu Wu | Kuan-Yu Chen
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)

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GALs: 基於對抗式學習之整列式摘要法 (GALs: A GAN-based Listwise Summarizer)
Chia-Chih Kuo | Kuan-Yu Chen
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)

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基於特徵粒度之訓練策略於中文口語問答系統之應用 (A Feature-granularity Training Strategy for Chinese Spoken Question Answering)
Shang-Bao Luo | Kuan-Yu Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 24, Number 2, December 2019

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EBSUM: 基於 BERT 的強健性抽取式摘要法 (EBSUM: An Enhanced BERT-based Extractive Summarization Framework)
Zheng-Yu Wu | Kuan-Yu Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 24, Number 2, December 2019

2018

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未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An OOV Word Embedding Framework for Chinese Machine Reading Comprehension)
Shang-Bao Luo | Ching-Hsien Lee | Jia-Jang Tu | Kuan-Yu Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 23, Number 2, December 2018

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未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An OOV Word Embedding Framework for Chinese Machine Reading Comprehension) [In Chinese]
Shang-Bao Luo | Ching-Hsien Lee | Kuan-Yu Chen
Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018)

2017

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使用查詢意向探索與類神經網路於語音文件檢索之研究 (Exploring Query Intent and Neural Network modeling Techniques for Spoken Document Retrieval) [In Chinese]
Tien-Hong Lo | Ying-Wen Chen | Berlin Chen | Kuan-Yu Chen | Hsin-Min Wang
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)

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當代非監督式方法之比較於節錄式語音摘要 (An Empirical Comparison of Contemporary Unsupervised Approaches for Extractive Speech Summarization) [In Chinese]
Shih-Hung Liu | Kuan-Yu Chen | Kai-Wun Shih | Berlin Chen | Hsin-Min Wang | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 22, Number 1, June 2017

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語音文件檢索使用類神經網路技術 (On the Use of Neural Network Modeling Techniques for Spoken Document Retrieval) [In Chinese]
Tien-Hong Lo | Ying-Wen Chen | Kuan-Yu Chen | Hsin-Min Wang | Berlin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 22, Number 2, December 2017-Special Issue on Selected Papers from ROCLING XXIX

2016

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Learning to Distill: The Essence Vector Modeling Framework
Kuan-Yu Chen | Shih-Hung Liu | Berlin Chen | Hsin-Min Wang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Learning representations of words is a pioneering study in this school of research. However, paragraph (or sentence and document) embedding learning is more suitable/reasonable for some tasks, such as sentiment classification and document summarization. Nevertheless, as far as we are aware, there is only a dearth of research focusing on launching unsupervised paragraph embedding methods. Classic paragraph embedding methods infer the representation of a given paragraph by considering all of the words occurring in the paragraph. Consequently, those stop or function words that occur frequently may mislead the embedding learning process to produce a misty paragraph representation. Motivated by these observations, our major contributions are twofold. First, we propose a novel unsupervised paragraph embedding method, named the essence vector (EV) model, which aims at not only distilling the most representative information from a paragraph but also excluding the general background information to produce a more informative low-dimensional vector representation for the paragraph. We evaluate the proposed EV model on benchmark sentiment classification and multi-document summarization tasks. The experimental results demonstrate the effectiveness and applicability of the proposed embedding method. Second, in view of the increasing importance of spoken content processing, an extension of the EV model, named the denoising essence vector (D-EV) model, is proposed. The D-EV model not only inherits the advantages of the EV model but also can infer a more robust representation for a given spoken paragraph against imperfect speech recognition. The utility of the D-EV model is evaluated on a spoken document summarization task, confirming the effectiveness of the proposed embedding method in relation to several well-practiced and state-of-the-art summarization methods.

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融合多任務學習類神經網路聲學模型訓練於會議語音辨識之研究(Leveraging Multi-task Learning with Neural Network Based Acoustic Modeling for Improved Meeting Speech Recognition) [In Chinese]
Ming-Han Yang | Yao-Chi Hsu | Hsiao-Tsung Hung | Ying-Wen Chen | Berlin Chen | Kuan-Yu Chen
Proceedings of the 28th Conference on Computational Linguistics and Speech Processing (ROCLING 2016)

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運用序列到序列生成架構於重寫式自動摘要(Exploiting Sequence-to-Sequence Generation Framework for Automatic Abstractive Summarization)[In Chinese]
Yu-Lun Hsieh | Shih-Hung Liu | Kuan-Yu Chen | Hsin-Min Wang | Wen-Lian Hsu | Berlin Chen
Proceedings of the 28th Conference on Computational Linguistics and Speech Processing (ROCLING 2016)

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評估尺度相關最佳化方法於華語錯誤發音檢測之研究 (Evaluation Metric-related Optimization Methods for Mandarin Mispronunciation Detection) [In Chinese]
Yao-Chi Hsu | Ming-Han Yang | Hsiao-Tsung Hung | Yi-Ju Lin | Kuan-Yu Chen | Berlin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016

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融合多任務學習類神經網路聲學模型訓練於會議語音辨識之研究 (Leveraging Multi-Task Learning with Neural Network Based Acoustic Modeling for Improved Meeting Speech Recognition) [In Chinese]
Ming-Han Yang | Yao-Chi Hsu | Hsiao-Tsung Hung | Ying-Wen Chen | Kuan-Yu Chen | Berlin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016

2015

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Proceedings of the ACL-IJCNLP 2015 Student Research Workshop
Kuan-Yu Chen | Angelina Ivanova | Ellie Pavlick | Emily Bender | Chin-Yew Lin | Stephan Oepen
Proceedings of the ACL-IJCNLP 2015 Student Research Workshop

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表示法學習技術於節錄式語音文件摘要之研究(A Study on Representation Learning Techniques for Extractive Spoken Document Summarization) [In Chinese]
Kai-Wun Shih | Berlin Chen | Kuan-Yu Chen | Shih-Hung Liu | Hsin-Min Wang
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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使用詞向量表示與概念資訊於中文大詞彙連續語音辨識之語言模型調適(Exploring Word Embedding and Concept Information for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]
Ssu-Cheng Chen | Kuan-Yu Chen | Hsiao-Tsung Hung | Berlin Chen
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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可讀性預測於中小學國語文教科書及優良課外讀物之研究(A Study of Readability Prediction on Elementary and Secondary Chinese Textbooks and Excellent Extracurricular Reading Materials) [In Chinese]
Yi-Nian Liu | Kuan-Yu Chen | Hou-Chiang Tseng | Berlin Chen
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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調變頻譜分解之改良於強健性語音辨識(Several Refinements of Modulation Spectrum Factorization for Robust Speech Recognition) [In Chinese]
Ting-Hao Chang | Hsiao-Tsung Hung | Kuan-Yu Chen | Hsin-Min Wang | Berlin Chen
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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節錄式語音文件摘要使用表示法學習技術 (Extractive Spoken Document Summarization with Representation Learning Techniques) [In Chinese]
Kai-Wun Shih | Kuan-Yu Chen | Shih-Hung Liu | Hsin-Min Wang | Berlin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015 - Special Issue on Selected Papers from ROCLING XXVII

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調變頻譜分解技術於強健語音辨識之研究 (Investigating Modulation Spectrum Factorization Techniques for Robust Speech Recognition) [In Chinese]
Ting-Hao Chang | Hsiao-Tsung Hung | Kuan-Yu Chen | Hsin-Min Wang | Berlin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015 - Special Issue on Selected Papers from ROCLING XXVII

2014

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Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization
Kuan-Yu Chen | Shih-Hung Liu | Berlin Chen | Ea-Ee Jan | Hsin-Min Wang | Wen-Lian Hsu | Hsin-Hsi Chen
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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探究新穎語句模型化技術於節錄式語音摘要 (Investigating Novel Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]
Shih-Hung Liu | Kuan-Yu Chen | Yu-Lun Hsieh | Berlin Chen | Hsin-Min Wang | Wen-Lian Hsu
Proceedings of the 26th Conference on Computational Linguistics and Speech Processing (ROCLING 2014)

2013

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Semantic Naïve Bayes Classifier for Document Classification
How Jing | Yu Tsao | Kuan-Yu Chen | Hsin-Min Wang
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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改良語句模型技術於節錄式語音摘要之研究 (Improved Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]
Shih-Hung Liu | Kuan-Yu Chen | Hsin-Min Wang | Wen-Lian Hsu | Berlin Chen
Proceedings of the 25th Conference on Computational Linguistics and Speech Processing (ROCLING 2013)

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A Study of Language Modeling for Chinese Spelling Check
Kuan-Yu Chen | Hung-Shin Lee | Chung-Han Lee | Hsin-Min Wang | Hsin-Hsi Chen
Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing

2011

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實證探究多種鑑別式語言模型於語音辨識之研究 (Empirical Comparisons of Various Discriminative Language Models for Speech Recognition) [In Chinese]
Min-Hsuan Lai | Bang-Xuan Huang | Kuan-Yu Chen | Berlin Chen
Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing (ROCLING 2011)

2009

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主題語言模型於大詞彙連續語音辨識之研究 (On the Use of Topic Models for Large-Vocabulary Continuous Speech Recognition) [In Chinese]
Kuan-Yu Chen | Berlin Chen
Proceedings of the 21st Conference on Computational Linguistics and Speech Processing