Haejun Lee


2020

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SLM: Learning a Discourse Language Representation with Sentence Unshuffling
Haejun Lee | Drew A. Hudson | Kangwook Lee | Christopher D. Manning
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other. However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them. To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins.

2018

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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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A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension
Seunghak Yu | Sathish Reddy Indurthi | Seohyun Back | Haejun Lee
Proceedings of the Workshop on Machine Reading for Question Answering

Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.

2017

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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.