Ruey-Cheng Chen


2020

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Incorporating Behavioral Hypotheses for Query Generation
Ruey-Cheng Chen | Chia-Jung Lee
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Generative neural networks have been shown effective on query suggestion. Commonly posed as a conditional generation problem, the task aims to leverage earlier inputs from users in a search session to predict queries that they will likely issue at a later time. User inputs come in various forms such as querying and clicking, each of which can imply different semantic signals channeled through the corresponding behavioral patterns. This paper induces these behavioral biases as hypotheses for query generation, where a generic encoder-decoder Transformer framework is presented to aggregate arbitrary hypotheses of choice. Our experimental results show that the proposed approach leads to significant improvements on top-k word error rate and Bert F1 Score compared to a recent BART model.

2013

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An improved MDL-based compression algorithm for unsupervised word segmentation
Ruey-Cheng Chen
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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A Regularized Compression Method to Unsupervised Word Segmentation
Ruey-Cheng Chen | Chiung-Min Tsai | Jieh Hsiang
Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology

2009

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Web Mining for Unsupervised Classification
Wei-Yen Day | Chun-Yi Chi | Ruey-Cheng Chen | Pu-Jen Cheng | Pei-Sen Liu
Proceedings of the 21st Conference on Computational Linguistics and Speech Processing

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Query Formulation by Selecting Good Terms
Chia-Jung Lee | Yi-Chun Lin | Ruey-Cheng Chen | Pei-Sen Liu | Pu-Jen Cheng
Proceedings of the 21st Conference on Computational Linguistics and Speech Processing