2015 - IEEE Fellow For contributions to human-machine interactions
Pascale Fung mainly focuses on Artificial intelligence, Natural language processing, Speech recognition, Word and Parallel corpora. Her Artificial intelligence study frequently links to related topics such as Compiler. Her Natural language processing study frequently involves adjacent topics like Character.
Her Speech recognition study combines topics from a wide range of disciplines, such as Sentence, Pronunciation, Conversation and Mandarin Chinese. Pascale Fung has researched Word in several fields, including Terminology, Translation and Identity. The Parallel corpora study which covers Pattern matching that intersects with Noun phrase, Proper noun, Word lists by frequency, Noun and Dynamic time warping.
Pascale Fung mainly investigates Artificial intelligence, Natural language processing, Speech recognition, Language model and Word. Her Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition. Her biological study spans a wide range of topics, including Feature and Mixed language.
Pascale Fung has included themes like Pronunciation and Mandarin Chinese in her Speech recognition study. The Language model study combines topics in areas such as Code-switching, Cache language model, Factored language model and Code. Pascale Fung specializes in Word, namely Bilingual lexicon.
Her scientific interests lie mostly in Artificial intelligence, Natural language processing, Language model, Machine learning and Word. Her Named-entity recognition research extends to the thematically linked field of Artificial intelligence. Her work deals with themes such as SemEval and Benchmark, which intersect with Natural language processing.
Her Language model research is included under the broader classification of Speech recognition. In general Speech recognition study, her work on Word error rate often relates to the realm of Transfer, thereby connecting several areas of interest. She combines subjects such as Sentence, Czech and Phrase with her study of Leverage.
Her primary areas of study are Artificial intelligence, Natural language processing, Language model, Code and Adaptation. Her work on Machine learning expands to the thematically related Artificial intelligence. Her Natural language processing research is multidisciplinary, incorporating perspectives in Word and Conversation.
Her Code research integrates issues from Question answering, Multi-document summarization, Information retrieval and Automatic summarization. Her Adaptation study incorporates themes from Dialog box, Joint and Word error rate. As a member of one scientific family, Pascale Fung mostly works in the field of Indonesian, focusing on Benchmark and, on occasion, Speech recognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
An IR Approach for Translating New Words from Nonparallel, Comparable Texts
Pascale Fung;Lo Yuen Yee.
meeting of the association for computational linguistics (1998)
A Statistical View on Bilingual Lexicon Extraction: From Parallel Corpora to Non-parallel Corpora
Pascale Fung.
conference of the association for machine translation in the americas (1998)
Statistical View on Bilingual Lexicon Extraction : From Parallel Corpora to Non-parallel Corpora
P. Fung.
Lecture Notes in Artificial Intelligence (1998)
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Chien-Sheng Wu;Andrea Madotto;Ehsan Hosseini-Asl;Caiming Xiong.
meeting of the association for computational linguistics (2019)
Finding Terminology Translations from Non-parallel Corpora
Pascale Fung.
Journal of Visual Languages and Computing (1997)
K-vec: a new approach for aligning parallel texts
Pascale Fung;Kenneth Ward Church.
international conference on computational linguistics (1994)
Compiling Bilingual Lexicon Entries From a Non-Parallel English-Chinese Corpus
Pascale N. Fung.
meeting of the association for computational linguistics (1995)
Overview for the First Shared Task on Language Identification in Code-Switched Data
Thamar Solorio;Elizabeth Blair;Suraj Maharjan;Steven Bethard.
workshop on computational approaches to code switching (2014)
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems
Andrea Madotto;Chien-Sheng Wu;Pascale Fung.
meeting of the association for computational linguistics (2018)
Mining Very-Non-Parallel Corpora: Parallel Sentence and Lexicon Extraction via Bootstrapping and E
Pascale Fung;Percy Cheung.
empirical methods in natural language processing (2004)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
University of Cambridge
Brown University
Columbia University
Baidu (China)
University of Paris-Saclay
Kyoto University
Salesforce (United States)
Raytheon (United States)
Facebook (United States)
Nvidia (United States)
University of Manchester
Harvard University
Technical University of Denmark
Purdue University West Lafayette
Kyoto University
Imperial College London
University of Saskatchewan
Texas A&M University
University of Georgia
University of Innsbruck
Pennsylvania State University
Tufts University
Washington University in St. Louis
University of Washington
George Mason University
University of Illinois at Chicago