Joo-Kyung Kim


2018

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A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding
Young-Bum Kim | Dongchan Kim | Joo-Kyung Kim | Ruhi Sarikaya
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale. In this paper, we propose a set of efficient and scalable shortlisting-reranking neural models for effective large-scale domain classification for IPDAs. The shortlisting stage focuses on efficiently trimming all domains down to a list of k-best candidate domains, and the reranking stage performs a list-wise reranking of the initial k-best domains with additional contextual information. We show the effectiveness of our approach with extensive experiments on 1,500 IPDA domains.

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Supervised Domain Enablement Attention for Personalized Domain Classification
Joo-Kyung Kim | Young-Bum Kim
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In large-scale domain classification for natural language understanding, leveraging each user’s domain enablement information, which refers to the preferred or authenticated domains by the user, with attention mechanism has been shown to improve the overall domain classification performance. In this paper, we propose a supervised enablement attention mechanism, which utilizes sigmoid activation for the attention weighting so that the attention can be computed with more expressive power without the weight sum constraint of softmax attention. The attention weights are explicitly encouraged to be similar to the corresponding elements of the output one-hot vector, and self-distillation is used to leverage the attention information of the other enabled domains. By evaluating on the actual utterances from a large-scale IPDA, we show that our approach significantly improves domain classification performance

2017

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Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources
Joo-Kyung Kim | Young-Bum Kim | Ruhi Sarikaya | Eric Fosler-Lussier
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target language. Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.

2016

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Adjusting Word Embeddings with Semantic Intensity Orders
Joo-Kyung Kim | Marie-Catherine de Marneffe | Eric Fosler-Lussier
Proceedings of the 1st Workshop on Representation Learning for NLP

2015

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Neural word embeddings with multiplicative feature interactions for tensor-based compositions
Joo-Kyung Kim | Marie-Catherine de Marneffe | Eric Fosler-Lussier
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

2013

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Deriving Adjectival Scales from Continuous Space Word Representations
Joo-Kyung Kim | Marie-Catherine de Marneffe
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing