Richard Tzong-Han Tsai

Also published as: Richard Tzong-han Tsai, Tzong-Han Richard Tsai, Tzong-Han Tsai


2024

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Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems
Bo-Han Lu | Yi-Hsuan Lin | Annie Lee | Richard Tzong-Han Tsai
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Machine translation focuses mainly on high-resource languages (HRLs), while low-resource languages (LRLs) like Taiwanese Hokkien are relatively under-explored. The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English. We employ a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage the orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin Chinese. Our comprehensive experiments involve translation tasks across various writing systems of Taiwanese Hokkien as well as between Taiwanese Hokkien and other HRLs. We find that the use of a limited monolingual corpus still further improves the model’s Taiwanese Hokkien capabilities. We then utilize our translation model to standardize all Taiwanese Hokkien writing systems into Hokkien Han, resulting in further performance improvements. Additionally, we introduce an evaluation method incorporating back-translation and GPT-4 to ensure reliable translation quality assessment even for LRLs. The study contributes to narrowing the resource gap for Taiwanese Hokkien and empirically investigates the advantages and limitations of pre-training and fine-tuning based on LLaMA 2.

2022

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Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien
Sin-En Lu | Bo-Han Lu | Chao-Yi Lu | Richard Tzong-Han Tsai
Findings of the Association for Computational Linguistics: EMNLP 2022

In natural language processing (NLP), code-mixing (CM) is a challenging task, especially when the mixed languages include dialects. In Southeast Asian countries such as Singapore, Indonesia, and Malaysia, Hokkien-Mandarin is the most widespread code-mixed language pair among Chinese immigrants, and it is also common in Taiwan. However, dialects such as Hokkien often have a scarcity of resources and the lack of an official writing system, limiting the development of dialect CM research. In this paper, we propose a method to construct a Hokkien-Mandarin CM dataset to mitigate the limitation, overcome the morphological issue under the Sino-Tibetan language family, and offer an efficient Hokkien word segmentation method through a linguistics-based toolkit. Furthermore, we use our proposed dataset and employ transfer learning to train the XLM (cross-lingual language model) for translation tasks. To fit the code-mixing scenario, we adapt XLM slightly. We found that by using linguistic knowledge, rules, and language tags, the model produces good results on CM data translation while maintaining monolingual translation quality.

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BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset
Nanda Putri Romadhona | Sin-En Lu | Bo-Han Lu | Richard Tzong-Han Tsai
Proceedings of the 29th International Conference on Computational Linguistics

Code-mixing refers to the mixed use of multiple languages. It is prevalent in multilingual societies and is also one of the most challenging natural language processing tasks. In this paper, we study Bahasa Rojak, a dialect popular in Malaysia that consists of English, Malay, and Chinese. Aiming to establish a model to deal with the code-mixing phenomena of Bahasa Rojak, we use data augmentation to automatically construct the first Bahasa Rojak corpus for pre-training language models, which we name the Bahasa Rojak Crawled Corpus (BRCC). We also develop a new pre-trained model called “Mixed XLM”. The model can tag the language of the input token automatically to process code-mixing input. Finally, to test the effectiveness of the Mixed XLM model pre-trained on BRCC for social media scenarios where code-mixing is found frequently, we compile a new Bahasa Rojak sentiment analysis dataset, SentiBahasaRojak, with a Kappa value of 0.77.

2021

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Verdict Inference with Claim and Retrieved Elements Using RoBERTa
In-Zu Gi | Ting-Yu Fang | Richard Tzong-Han Tsai
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

Automatic fact verification has attracted recent research attention as the increasing dissemination of disinformation on social media platforms. The FEVEROUS shared task introduces a benchmark for fact verification, in which a system is challenged to verify the given claim using the extracted evidential elements from Wikipedia documents. In this paper, we propose our 3rd place three-stage system consisting of document retrieval, element retrieval, and verdict inference for the FEVEROUS shared task. By considering the context relevance in the fact extraction and verification task, our system achieves 0.29 FEVEROUS score on the development set and 0.25 FEVEROUS score on the blind test set, both outperforming the FEVEROUS baseline.

2018

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Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding
Chun-Kai Wu | Richard Tzong-Han Tsai
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62%. Tested 30 times, our system achieved an average improvement of 2.76% over the current best system (26.86% over baseline), a statistically significant result.

2017

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Encyclolink: A Cross-Encyclopedia,Cross-language Article-Linking System and Web-based Search Interface
Yu-Chun Wang | Ka Ming Wong | Chun-Kai Wu | Chao-Lin Pan | Richard Tzong-Han Tsai
Proceedings of the IJCNLP 2017, System Demonstrations

Cross-language article linking (CLAL) is the task of finding corresponding article pairs across encyclopedias of different languages. In this paper, we present Encyclolink, a web-based CLAL search interface designed to help users find equivalent encyclopedia articles in Baidu Baike for a given English Wikipedia article title query. Encyclolink is powered by our cross-encyclopedia entity embedding CLAL system (0.8 MRR). The browser-based Interface provides users with a clear and easily readable preview of the contents of retrieved articles for comparison.

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A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification
Jui-Yang Wang | Min-Feng Kuo | Jen-Chieh Han | Chao-Chuang Shih | Chun-Hsun Chen | Po-Ching Lee | Richard Tzong-Han Tsai
Proceedings of the IJCNLP 2017, System Demonstrations

In the paper, we propose an information retrieval based (IR-based) Question Answering (QA) system to assist online customer service staffs respond users in the telecom domain. When user asks a question, the system retrieves a set of relevant answers and ranks them. Moreover, our system uses a novel reranker to enhance the ranking result of information retrieval. It employs the word2vec model to represent the sentences as vectors. It also uses a sub-category feature, predicted by the k-nearest neighbor algorithm. Finally, the system returns the top five candidate answers, making online staffs find answers much more efficiently.

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Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble
Jing Cyun Tu | Po-Ting Lai | Richard Tzong-Han Tsai
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

The study of drug-drug interaction (DDI) is important in the drug discovering. Both PubMed and DrugBank are rich resources to retrieve DDI information which is usually represented in plain text. Automatically extracting DDI pairs from text improves the quality of drug discov-ering. In this paper, we presented a study that focuses on the DDI classification. We normalized the drug names, and developed both sentence-level and corpus-level features for DDI classification. A classifier ensemble approach is used for the unbalance DDI labels problem. Our approach achieved an F-score of 65.4% on SemEval 2013 DDI test set. The experimental results also show the effects of proposed corpus-level features in the DDI task.

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Question Retrieval with Distributed Representations and Participant Reputation in Community Question Answering
Sam Weng | Kevin Chun-Kai Wu | Yu-Chun Wang | Richard Tzong-Han Tsai
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)

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Question Retrieval with Distributed Representations and Participant Reputation in Community Question Answering
Sam Weng | Chun-Kai Wu | Yu-Chun Wang | Richard Tzong-Han Tsai
International Journal of Computational Linguistics & Chinese Language Processing, Volume 22, Number 2, December 2017-Special Issue on Selected Papers from ROCLING XXIX

2015

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基於 Word2Vec 詞向量的網路情緒文和流行音樂媒合方法之研究(Matching Internet Mood Essays with Pop-Music Based on Word2Vec)[In Chinese]
Pin-Chu Wen | Yi-Lin Tsai | Tzong-Han Tsai
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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NCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches
Yu-Chun Wang | Chun-Kai Wu | Richard Tzong-Han Tsai
Proceedings of the Fifth Named Entity Workshop

2014

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Cross-language and Cross-encyclopedia Article Linking Using Mixed-language Topic Model and Hypernym Translation
Yu-Chun Wang | Chun-Kai Wu | Richard Tzong-Han Tsai
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Joint Learning of Entity Linking Constraints Using a Markov-Logic Network
Hong-Jie Dai | Richard Tzong-Han Tsai | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 1, March 2014

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Transliteration Extraction from Classical Chinese Buddhist Literature Using Conditional Random Fields with Language Models
Yu-Chun Wang | Karol Chia-Tien Chang | Richard Tzong-Han Tsai | Jieh Hsiang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 3, September 2014

2013

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Transliteration Extraction from Classical Chinese Buddhist Literature Using Conditional Random Fields
Yu-Chun Wang | Richard Tzong-Han Tsai
Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC 27)

2012

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Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING 2012)
Richard Tzong-Han Tsai | Liang-Chih Yu
Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING 2012)

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Enhancement of Feature Engineering for Conditional Random Field Learning in Chinese Word Segmentation Using Unlabeled Data
Mike Tian-Jian Jiang | Cheng-Wei Shih | Ting-Hao Yang | Chan-Hung Kuo | Richard Tzong-Han Tsai | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 17, Number 3, September 2012

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International Journal of Computational Linguistics & Chinese Language Processing, Volume 17, Number 4, December 2012-Special Issue on Selected Papers from ROCLING XXIV
Liang-Chih Yu | Richard Tzong-Han Tsai | Chia-Ping Chen | Cheng-Zen Yang | Shu-Kai Hsieh
International Journal of Computational Linguistics & Chinese Language Processing, Volume 17, Number 4, December 2012-Special Issue on Selected Papers from ROCLING XXIV

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English-Korean Named Entity Transliteration Using Substring Alignment and Re-ranking Methods
Chun-Kai Wu | Yu-Chun Wang | Richard Tzong-Han Tsai
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

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Unsupervised Japanese-Chinese Opinion Word Translation using Dependency Distance and Feature-Opinion Association Weight
Guo-Hau Lai | Ying-Mei Guo | Richard Tzong-Han Tsai
Proceedings of COLING 2012

2011

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Evaluation via Negativa of Chinese Word Segmentation for Information Retrieval
Mike Tian-Jian Jiang | Cheng-Wei Shih | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

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English-Korean Named Entity Transliteration Using Statistical Substring-based and Rule-based Approaches
Yu-Chun Wang | Richard Tzong-Han Tsai
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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Entity Disambiguation Using a Markov-Logic Network
Hong-Jie Dai | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of 5th International Joint Conference on Natural Language Processing

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Unsupervised Overlapping Feature Selection for Conditional Random Fields Learning in Chinese Word Segmentation
Ting-hao Yang | Tian-Jian Jiang | Chan-hung Kuo | Richard Tzong-han Tsai | Wen-lian Hsu
Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing (ROCLING 2011)

2010

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基於非監督式詞義消歧之日語旅遊意見詞翻譯 (Japanese Opinion Word Translation Based on Unsupervised Word Sense Disambiguation in the Travel Domain) [In Chinese]
Jyun-Wei Huang | Chia Pei Kao | Chun-Yu Chen | Tzong-Han Tsai
Proceedings of the 22nd Conference on Computational Linguistics and Speech Processing (ROCLING 2010)

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Global Ranking via Data Fusion
Hong-Jie Dai | Po-Ting Lai | Richard Tzong-Han Tsai | Wen-Lian Hsu
Coling 2010: Posters

2009

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WikiSense: Supersense Tagging of Wikipedia Named Entities Based WordNet
Joseph Chang | Richard Tzong-Han Tsai | Jason S. Chang
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Modeling the Relationship among Linguistic Typological Features with Hierarchical Dirichlet Process
Chu-Cheng Lin | Yu-Chun Wang | Richard Tzong-Han Tsai
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Rule-based Korean Grapheme to Phoneme Conversion Using Sound Patterns
Yu-Chun Wang | Richard Tzong-Han Tsai
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Minimally Supervised Question Classification and Answering based on WordNet and Wikipedia
Joseph Chang | Tzu-Hsi Yen | Tzong-Han Tsai
Proceedings of the 21st Conference on Computational Linguistics and Speech Processing

2008

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Generating Patterns for Extracting Chinese-Korean Named Entity Translations from theWeb
Chih-Hao Yeh | Wei-Chi Tsai | Yu-Chun Wang | Richard Tzong-Han Tsai
ROCLING 2008 Poster Papers

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Learning Patterns from the Web to Translate Named Entities for Cross Language Information Retrieval
Yu-Chun Wang | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Exploiting Unlabeled Text to Extract New Words of Different Semantic Transparency for Chinese Word Segmentation
Richard Tzong-Han Tsai | Hsi-Chuan Hung
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

2007

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Korean-Chinese Person Name Translation for Cross Language Information Retrieval
Yu-Chun Wang | Yi-Hsun Lee | Chu-Cheng Lin | Tzong-Han Richard Tsai | Wen-Lian Hsu
Proceedings of the 21st Pacific Asia Conference on Language, Information and Computation

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Korean-Chinese Cross-Language Information Retrieval Based on Extension of Dictionaries and Transliteration
Yu-Chun Wang | Tzong-Han Richard Tsai | Hsu-Chun Yen | Wen-Lian Hsu
Proceedings of the 19th Conference on Computational Linguistics and Speech Processing

2006

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A Hybrid Approach to Biomedical Named Entity Recognition and Semantic Role Labeling
Richard Tzong-Han Tsai
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Doctoral Consortium

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On Closed Task of Chinese Word Segmentation: An Improved CRF Model Coupled with Character Clustering and Automatically Generated Template Matching
Richard Tzong-Han Tsai | Hsieh-Chuan Hung | Cheng-Lung Sung | Hong-Jie Dai | Wen-Lian Hsu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

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On Using Ensemble Methods for Chinese Named Entity Recognition
Chia-Wei Wu | Shyh-Yi Jan | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

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A Semi-Automatic Method for Annotating a Biomedical Proposition Bank
Wen-Chi Chou | Richard Tzong-Han Tsai | Ying-Shan Su | Wei Ku | Ting-Yi Sung | Wen-Lian Hsu
Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006

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BIOSMILE: Adapting Semantic Role Labeling for Biomedical Verbs:
Richard Tzong-Han Tsai | Wen-Chi Chou | Yu-Chun Lin | Cheng-Lung Sung | Wei Ku | Ying-Shan Su | Ting-Yi Sung | Wen-Lian Hsu
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology

2005

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Using Maximum Entropy to Extract Biomedical Named Entities without Dictionaries
Tzong-Han Tsai | Chia-Wei Wu | Wen-Lian Hsu
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

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Exploiting Full Parsing Information to Label Semantic Roles Using an Ensemble of ME and SVM via Integer Linear Programming
Tzong-Han Tsai | Chia-Wei Wu | Yu-Chun Lin | Wen-Lian Hsu
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Applying Maximum Entropy to Robust Chinese Shallow Parsing
Shih-Hung Wu | Cheng-Wei Shih | Chia-Wei Wu | Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the 17th Conference on Computational Linguistics and Speech Processing

2004

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The Construction of a Chinese Named Entity Tagged Corpus: CNEC1.0
Cheng-Wei Shih | Tzong-Han Tsai | Shih-Hung Wu | Chiu-Chen Hsieh | Wen-Lian Hsu
Proceedings of the 16th Conference on Computational Linguistics and Speech Processing

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Mencius: A Chinese Named Entity Recognizer Using the Maximum Entropy-based Hybrid Model
Tzong-Han Tsai | Shih-Hung Wu | Cheng-Wei Lee | Cheng-Wei Shih | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 9, Number 1, February 2004: Special Issue on Selected Papers from ROCLING XV

2003

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Mencius: A Chinese Named Entity Recognizer Using Hybrid Model
Tzong-Han Tsai | Shih-Hung Wu | Wen-Lian Hsu
Proceedings of Research on Computational Linguistics Conference XV

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Text Categorization Using Automatically Acquired Domain Ontology
Shih-Hung Wu | Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the Sixth International Workshop on Information Retrieval with Asian Languages