Hyun Kim


ANNA: Enhanced Language Representation for Question Answering
Changwook Jun | Hansol Jang | Myoseop Sim | Hyun Kim | Jooyoung Choi | Kyungkoo Min | Kyunghoon Bae
Proceedings of the 7th Workshop on Representation Learning for NLP

Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks. Most existing models performing state-of-the-art results have shown their approaches in the separate perspectives of data processing, pre-training tasks, neural network modeling, or fine-tuning. In this paper, we demonstrate how the approaches affect performance individually, and that the language model performs the best results on a specific question answering task when those approaches are jointly considered in pre-training models. In particular, we propose an extended pre-training task, and a new neighbor-aware mechanism that attends neighboring tokens more to capture the richness of context for pre-training language modeling. Our best model achieves new state-of-the-art results of 95.7% F1 and 90.6% EM on SQuAD 1.1 and also outperforms existing pre-trained language models such as RoBERTa, ALBERT, ELECTRA, and XLNet on the SQuAD 2.0 benchmark.

Korean-Specific Dataset for Table Question Answering
Changwook Jun | Jooyoung Choi | Myoseop Sim | Hyun Kim | Hansol Jang | Kyungkoo Min
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Existing question answering systems mainly focus on dealing with text data. However, much of the data produced daily is stored in the form of tables that can be found in documents and relational databases, or on the web. To solve the task of question answering over tables, there exist many datasets for table question answering written in English, but few Korean datasets. In this paper, we demonstrate how we construct Korean-specific datasets for table question answering: Korean tabular dataset is a collection of 1.4M tables with corresponding descriptions for unsupervised pre-training language models. Korean table question answering corpus consists of 70k pairs of questions and answers created by crowd-sourced workers. Subsequently, we then build a pre-trained language model based on Transformer and fine-tune the model for table question answering with these datasets. We then report the evaluation results of our model. We make our datasets publicly available via our GitHub repository and hope that those datasets will help further studies for question answering over tables, and for the transformation of table formats.


QE BERT: Bilingual BERT Using Multi-task Learning for Neural Quality Estimation
Hyun Kim | Joon-Ho Lim | Hyun-Ki Kim | Seung-Hoon Na
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

For translation quality estimation at word and sentence levels, this paper presents a novel approach based on BERT that recently has achieved impressive results on various natural language processing tasks. Our proposed model is re-purposed BERT for the translation quality estimation and uses multi-task learning for the sentence-level task and word-level subtasks (i.e., source word, target word, and target gap). Experimental results on Quality Estimation shared task of WMT19 show that our systems show competitive results and provide significant improvements over the baseline.


Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation
Hyun Kim | Jong-Hyeok Lee | Seung-Hoon Na
Proceedings of the Second Conference on Machine Translation


A Recurrent Neural Networks Approach for Estimating the Quality of Machine Translation Output
Hyun Kim | Jong-Hyeok Lee
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recurrent Neural Network based Translation Quality Estimation
Hyun Kim | Jong-Hyeok Lee
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers