Search Results for author: Dongyub Lee

Found 10 papers, 4 papers with code

Towards Reliable and Fluent Large Language Models: Incorporating Feedback Learning Loops in QA Systems

no code implementations8 Sep 2023 Dongyub Lee, Taesun Whang, Chanhee Lee, Heuiseok Lim

First, we build a dataset to train a critic model capable of evaluating the citation, correctness, and fluency of responses generated by LLMs in QA systems.

Response Generation

Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis

no code implementations ACL 2021 Shinhyeok Oh, Dongyub Lee, Taesun Whang, IlNam Park, Gaeun Seo, EungGyun Kim, Harksoo Kim

In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i. e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies).

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

Auxiliary Sequence Labeling Tasks for Disfluency Detection

no code implementations24 Oct 2020 Dongyub Lee, Byeongil Ko, Myeong Cheol Shin, Taesun Whang, Daniel Lee, Eun Hwa Kim, EungGyun Kim, Jaechoon Jo

Existing works for disfluency detection have focused on designing a single objective only for disfluency detection, while auxiliary objectives utilizing linguistic information of a word such as named entity or part-of-speech information can be effective.

named-entity-recognition Named Entity Recognition +4

Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization

no code implementations COLING 2020 Dongyub Lee, Myeongcheol Shin, Taesun Whang, Seungwoo Cho, Byeongil Ko, Daniel Lee, EungGyun Kim, Jaechoon Jo

In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS).

Text Summarization

EmotionX-KU: BERT-Max based Contextual Emotion Classifier

2 code implementations27 Jun 2019 Kisu Yang, Dongyub Lee, Taesun Whang, Seolhwa Lee, Heuiseok Lim

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.

Emotion Recognition Language Modelling

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