24 citations · 30 across the 6 of their papers we have counts for
7 papers
Towards Reliable and Fluent Large Language Models: Incorporating Feedback Learning Loops in QA Systems
Dongyub Lee, Taesun Whang, Chanhee Lee +1
Large language models (LLMs) have emerged as versatile tools in various daily applications. However, they are fraught with issues that undermine their utility and trustworthiness.…
Auxiliary Sequence Labeling Tasks for Disfluency Detection
Dongyub Lee, Byeongil Ko, Myeong Cheol Shin +5
Detecting disfluencies in spontaneous speech is an important preprocessing step in natural language processing and speech recognition applications. Existing works for disfluency de…
Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection
Taesun Whang, Dongyub Lee, Dongsuk Oh +4
In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained lan…
Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization
Dongyub Lee, Myeongcheol Shin, Taesun Whang +5
Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Many existing works for text summarization a…
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
Taesun Whang, Dongyub Lee, Chanhee Lee +3
We focus on multi-turn response selection in a retrieval-based dialog system. In this paper, we utilize the powerful pre-trained language model Bi-directional Encoder Representatio…
EmotionX-KU: BERT-Max based Contextual Emotion Classifier
Kisu Yang, Dongyub Lee, Taesun Whang +2
We propose a contextual emotion classifier based on a transferable language model and dynamic max pooling, which predicts the emotion of each utterance in a dialogue. A representat…
Character-Level Feature Extraction with Densely Connected Networks
Chanhee Lee, Young-Bum Kim, Dongyub Lee +1
Generating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generatin…