Huy Nguyen


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Robust Product Classification with Instance-Dependent Noise
Huy Nguyen | Devashish Khatwani
Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)

Noisy labels in large E-commerce product data (i.e., product items are placed into incorrect categories) is a critical issue for product categorization task because they are unavoidable, non-trivial to remove and degrade prediction performance significantly. Training a product title classification model which is robust to noisy labels in the data is very important to make product classification applications more practical. In this paper, we study the impact of instance-dependent noise to performance of product title classification by comparing our data denoising algorithm and different noise-resistance training algorithms which were designed to prevent a classifier model from over-fitting to noise. We develop a simple yet effective Deep Neural Network for product title classification to use as a base classifier. Along with recent methods of stimulating instance-dependent noise, we propose a novel noise stimulation algorithm based on product title similarity. Our experiments cover multiple datasets, various noise methods and different training solutions. Results uncover the limit of classification task when noise rate is not negligible and data distribution is highly skewed.


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Automated Essay Scoring with Discourse-Aware Neural Models
Farah Nadeem | Huy Nguyen | Yang Liu | Mari Ostendorf
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering. Neural networks offer an alternative to feature engineering, but they typically require more annotated data. This paper explores network structures, contextualized embeddings and pre-training strategies aimed at capturing discourse characteristics of essays. Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available.


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Context-aware Argumentative Relation Mining
Huy Nguyen | Diane Litman
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Instant Feedback for Increasing the Presence of Solutions in Peer Reviews
Huy Nguyen | Wenting Xiong | Diane Litman
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations


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Extracting Argument and Domain Words for Identifying Argument Components in Texts
Huy Nguyen | Diane Litman
Proceedings of the 2nd Workshop on Argumentation Mining


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Improving Peer Feedback Prediction: The Sentence Level is Right
Huy Nguyen | Diane Litman
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications


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Exploiting Context for Biomedical Entity Recognition: From Syntax to the Web
Jenny Finkel | Shipra Dingare | Huy Nguyen | Malvina Nissim | Christopher Manning | Gail Sinclair
Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA/BioNLP)


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Named Entity Recognition with Character-Level Models
Dan Klein | Joseph Smarr | Huy Nguyen | Christopher D. Manning
Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003


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Automatic Semantic Grouping in a Spoken Language User Interface Toolkit
Hassan Alam | Hua Cheng | Rachmat Hartono | Aman Kumar | Paul Llido | Crystal Nakatsu | Huy Nguyen | Fuad Rahman | Yuliya Tarnikova | Timotius Tjahjadi | Che Wilcox
COLING 2002: The 19th International Conference on Computational Linguistics