most citedAutoregressive Score Generation for Multi-trait Essay Scoring

1 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024

Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards

Heejin Do, Sangwon Ryu, Gary Geunbae Lee

Recent advances in automated essay scoring (AES) have shifted towards evaluating multiple traits to provide enriched feedback. Like typical AES systems, multi-trait AES employs the…

cs.CL20241 cited

An Investigation Into Explainable Audio Hate Speech Detection

Jinmyeong An, Wonjun Lee, Yejin Jeon +3

Research on hate speech has predominantly revolved around detection and interpretation from textual inputs, leaving verbal content largely unexplored. While there has been limited…

cs.CL2024

DiagESC: Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation

Seungyeon Seo, Gary Geunbae Lee

Dialogue systems for mental health care aim to provide appropriate support to individuals experiencing mental distress. While extensive research has been conducted to deliver adequ…

cs.CL2024

Denoising Table-Text Retrieval for Open-Domain Question Answering

Deokhyung Kang, Baikjin Jung, Yunsu Kim +1

In table-text open-domain question answering, a retriever system retrieves relevant evidence from tables and text to answer questions. Previous studies in table-text open-domain qu…

cs.CL20241 cited

Autoregressive Score Generation for Multi-trait Essay Scoring

Heejin Do, Yunsu Kim, Gary Geunbae Lee

Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score. However, studies have yet…

cs.SD2024

Enhancing Zero-Shot Multi-Speaker TTS with Negated Speaker Representations

Yejin Jeon, Yunsu Kim, Gary Geunbae Lee

Zero-shot multi-speaker TTS aims to synthesize speech with the voice of a chosen target speaker without any fine-tuning. Prevailing methods, however, encounter limitations at adapt…