Mi-Young Kim


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Locally Distributed Activation Vectors for Guided Feature Attribution
Housam K. B. Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 29th International Conference on Computational Linguistics

Explaining the predictions of a deep neural network (DNN) is a challenging problem. Many attempts at interpreting those predictions have focused on attribution-based methods, which assess the contributions of individual features to each model prediction. However, attribution-based explanations do not always provide faithful explanations to the target model, e.g., noisy gradients can result in unfaithful feature attribution for back-propagation methods. We present a method to learn explanations-specific representations while constructing deep network models for text classification. These representations can be used to faithfully interpret black-box predictions, i.e., highlighting the most important input features and their role in any particular prediction. We show that learning specific representations improves model interpretability across various tasks, for both qualitative and quantitative evaluations, while preserving predictive performance.


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DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector
Housam Khalifa Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret. Even in applications of limited scope on fixed data, the creation of these complex “black-boxes” creates substantial challenges for debugging, understanding, and generalization. But rapid development in this field has now lead to building more straightforward and interpretable models. We propose a new technique (DISK-CSV) to distill knowledge concurrently from any neural network architecture for text classification, captured as a lightweight interpretable/explainable classifier. Across multiple datasets, our approach achieves better performance than the target black-box. In addition, our approach provides better explanations than existing techniques.


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RANCC: Rationalizing Neural Networks via Concept Clustering
Housam Khalifa Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 28th International Conference on Computational Linguistics

We propose a new self-explainable model for Natural Language Processing (NLP) text classification tasks. Our approach constructs explanations concurrently with the formulation of classification predictions. To do so, we extract a rationale from the text, then use it to predict a concept of interest as the final prediction. We provide three types of explanations: 1) rationale extraction, 2) a measure of feature importance, and 3) clustering of concepts. In addition, we show how our model can be compressed without applying complicated compression techniques. We experimentally demonstrate our explainability approach on a number of well-known text classification datasets.


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A Lexicalized Tree Kernel for Open Information Extraction
Ying Xu | Christoph Ringlstetter | Mi-Young Kim | Grzegorz Kondrak | Randy Goebel | Yusuke Miyao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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Open Information Extraction with Tree Kernels
Ying Xu | Mi-Young Kim | Kevin Quinn | Randy Goebel | Denilson Barbosa
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Transliteration Generation and Mining with Limited Training Resources
Sittichai Jiampojamarn | Kenneth Dwyer | Shane Bergsma | Aditya Bhargava | Qing Dou | Mi-Young Kim | Grzegorz Kondrak
Proceedings of the 2010 Named Entities Workshop


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Chunking Using Conditional Random Fields in Korean Texts
Yong-Hun Lee | Mi-Young Kim | Jong-Hyeok Lee
Second International Joint Conference on Natural Language Processing: Full Papers

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Two-Phase Shift-Reduce Deterministic Dependency Parser of Chinese
Meixun Jin | Mi-Young Kim | Jong-Hyeok Lee
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts


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Segmentation of Chinese Long Sentences Using Commas
Meixun Jin | Mi-Young Kim | Dongil Kim | Jong-Hyeok Lee
Proceedings of the Third SIGHAN Workshop on Chinese Language Processing


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S-clause segmentation for efficient syntactic analysis using decision trees
Mi-Young Kim | Jong-Hyeok Lee
Proceedings of the Australasian Language Technology Workshop 2003