Jihie Kim


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MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller
Seohyun Back | Seunghak Yu | Sathish Reddy Indurthi | Jihie Kim | Jaegul Choo
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text. Existing approaches made a significant progress comparable to human-level performance, but they are still limited in understanding, up to a few paragraphs, failing to properly comprehend lengthy document. In this paper, we propose a novel deep neural network architecture to handle a long-range dependency in RC tasks. In detail, our method has two novel aspects: (1) an advanced memory-augmented architecture and (2) an expanded gated recurrent unit with dense connections that mitigate potential information distortion occurring in the memory. Our proposed architecture is widely applicable to other models. We have performed extensive experiments with well-known benchmark datasets such as TriviaQA, QUASAR-T, and SQuAD. The experimental results demonstrate that the proposed method outperforms existing methods, especially for lengthy documents.

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On-Device Neural Language Model Based Word Prediction
Seunghak Yu | Nilesh Kulkarni | Haejun Lee | Jihie Kim
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation. However, deploying huge language models for the mobile device such as on-device keyboards poses computation as a bottle-neck due to their puny computation capacities. In this work, we propose an on-device neural language model based word prediction method that optimizes run-time memory and also provides a real-time prediction environment. Our model size is 7.40MB and has average prediction time of 6.47 ms. Our proposed model outperforms the existing methods for word prediction in terms of keystroke savings and word prediction rate and has been successfully commercialized.

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Self-Learning Architecture for Natural Language Generation
Hyungtak Choi | Siddarth K.M. | Haehun Yang | Heesik Jeon | Inchul Hwang | Jihie Kim
Proceedings of the 11th International Conference on Natural Language Generation

In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants. Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive. We examine three different models based on our proposed architecture - Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain - to reduce the human labor required for template generation. We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture. In both automatic and human evaluation, the self-learning architecture outperforms previous works trained with a fully human-labeled dataset. This is promising for commercial conversational assistant solutions.


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Syllable-level Neural Language Model for Agglutinative Language
Seunghak Yu | Nilesh Kulkarni | Haejun Lee | Jihie Kim
Proceedings of the First Workshop on Subword and Character Level Models in NLP

We introduce a novel method to diminish the problem of out of vocabulary words by introducing an embedding method which leverages the agglutinative property of language. We propose additional embedding derived from syllables and morphemes for the words to improve the performance of language model. We apply the above method to input prediction tasks and achieve state of the art performance in terms of Key Stroke Saving (KSS) w.r.t. to existing device input prediction methods.


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Modeling Message Roles and Influence in Q&A Forums
Jeonhyung Kang | Jihie Kim
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

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Towards Modeling Social and Content Dynamics in Discussion Forums
Jihie Kim | Aram Galstyan
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

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Towards Identifying Unresolved Discussions in Student Online Forums
Jihie Kim | Jia Li | Taehwan Kim
Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications


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Learning to Detect Conversation Focus of Threaded Discussions
Donghui Feng | Erin Shaw | Jihie Kim | Eduard Hovy
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference