Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Moon, Hyeonseok"'
Autor:
Jang, Yoonna, Son, Suhyune, Lee, Jeongwoo, Son, Junyoung, Hur, Yuna, Lim, Jungwoo, Moon, Hyeonseok, Yang, Kisu, Lim, Heuiseok
Despite the striking advances in recent language generation performance, model-generated responses have suffered from the chronic problem of hallucinations that are either untrue or unfaithful to a given source. Especially in the task of knowledge gr
Externí odkaz:
http://arxiv.org/abs/2406.10809
Translating major language resources to build minor language resources becomes a widely-used approach. Particularly in translating complex data points composed of multiple components, it is common to translate each component separately. However, we a
Externí odkaz:
http://arxiv.org/abs/2404.16257
Autor:
Koo, Seonmin, Park, Chanjun, Kim, Jinsung, Seo, Jaehyung, Eo, Sugyeong, Moon, Hyeonseok, Lim, Heuiseok
Automatic speech recognition (ASR) outcomes serve as input for downstream tasks, substantially impacting the satisfaction level of end-users. Hence, the diagnosis and enhancement of the vulnerabilities present in the ASR model bear significant import
Externí odkaz:
http://arxiv.org/abs/2401.14625
In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages. Our methodology centers on two core strategies: 1) language-specific data
Externí odkaz:
http://arxiv.org/abs/2306.14514
Autor:
Park, Chanjun, Koo, Seonmin, Lee, Seolhwa, Seo, Jaehyung, Eo, Sugyeong, Moon, Hyeonseok, Lim, Heuiseok
Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively. While recent attention has been given to data-centric AI based on synthetic data, due to its potenti
Externí odkaz:
http://arxiv.org/abs/2306.14377
Autor:
Eo, Sugyeong, Moon, Hyeonseok, Kim, Jinsung, Hur, Yuna, Kim, Jeongwook, Lee, Songeun, Chun, Changwoo, Park, Sungsoo, Lim, Heuiseok
Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. However, the diversity of QA types remains a challenge despite its contributions to comprehensive learning and assessment of childre
Externí odkaz:
http://arxiv.org/abs/2306.06605
Leaderboard systems allow researchers to objectively evaluate Natural Language Processing (NLP) models and are typically used to identify models that exhibit superior performance on a given task in a predetermined setting. However, we argue that eval
Externí odkaz:
http://arxiv.org/abs/2303.10888
Autor:
Eo, Sugyeong, Park, Chanjun, Moon, Hyeonseok, Seo, Jaehyung, Kim, Gyeongmin, Lee, Jungseob, Lim, Heuiseok
Publikováno v:
COLING 2022
With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing. QE aims to automatically predict the quality of machine translation (MT) output without reference s
Externí odkaz:
http://arxiv.org/abs/2209.15285
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions. Hence, we develop a self-supervised d
Externí odkaz:
http://arxiv.org/abs/2111.12284
Building of data for quality estimation (QE) training is expensive and requires significant human labor. In this study, we focus on a data-centric approach while performing QE, and subsequently propose a fully automatic pseudo-QE dataset generation t
Externí odkaz:
http://arxiv.org/abs/2111.00767