Hyungjong Noh


HaRiM+: Evaluating Summary Quality with Hallucination Risk
Seonil (Simon) Son | Junsoo Park | Jeong-in Hwang | Junghwa Lee | Hyungjong Noh | Yeonsoo Lee
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

One of the challenges of developing a summarization model arises from the difficulty in measuring the factual inconsistency of the generated text. In this study, we reinterpret the decoder overconfidence-regularizing objective suggested in (Miao et al., 2021) as a hallucination risk measurement to better estimate the quality of generated summaries. We propose a reference-free metric, HaRiM+, which only requires an off-the-shelf summarization model to compute the hallucination risk based on token likelihoods. Deploying it requires no additional training of models or ad-hoc modules, which usually need alignment to human judgments. For summary-quality estimation, HaRiM+ records state-of-the-art correlation to human judgment on three summary-quality annotation sets: FRANK, QAGS, and SummEval. We hope that our work, which merits the use of summarization models, facilitates the progress of both automated evaluation and generation of summary.

Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model
Hojun Cho | Dohee Kim | Seungwoo Ryu | ChaeHun Park | Hyungjong Noh | Jeong-in Hwang | Minseok Choi | Edward Choi | Jaegul Choo
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Style control, content preservation, and fluency determine the quality of text style transfer models. To train on a nonparallel corpus, several existing approaches aim to deceive the style discriminator with an adversarial loss. However, adversarial training significantly degrades fluency compared to the other two metrics. In this work, we explain this phenomenon using energy-based interpretation, and leverage a pretrained language model to improve fluency. Specifically, we propose a novel approach which applies the pretrained language model to the text style transfer framework by restructuring the discriminator and the model itself, allowing the generator and the discriminator to also take advantage of the power of the pretrained model. We evaluated our model on three public benchmarks GYAFC, Amazon, and Yelp and achieved state-of-the-art performance on the overall metrics.


POMY: A Conversational Virtual Environment for Language Learning in POSTECH
Hyungjong Noh | Kyusong Lee | Sungjin Lee | Gary Geunbae Lee
Proceedings of the SIGDIAL 2011 Conference


A Joint Statistical Model for Simultaneous Word Spacing and Spelling Error Correction for Korean
Hyungjong Noh | Jeong-Won Cha | Gary Geunbae Lee
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions