14 citations · 19 across the 7 of their papers we have counts for
10 papers
Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking
Jihyun Lee, Chaebin Lee, Yunsu Kim +1
In dialogue state tracking (DST), labeling the dataset involves considerable human labor. We propose a new self-training framework for few-shot generative DST that utilize unlabele…
Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions
Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee
Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to…
Schema Encoding for Transferable Dialogue State Tracking
Hyunmin Jeon, Gary Geunbae Lee
Dialogue state tracking (DST) is an essential sub-task for task-oriented dialogue systems. Recent work has focused on deep neural models for DST. However, the neural models require…
Conversational QA Dataset Generation with Answer Revision
Seonjeong Hwang, Gary Geunbae Lee
Conversational question--answer generation is a task that automatically generates a large-scale conversational question answering dataset based on input passages. In this paper, we…
SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task
Jihyun Lee, Gary Geunbae Lee
Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data. In this paper, we introduce an ontology-free…
DORA: Toward Policy Optimization for Task-oriented Dialogue System with Efficient Context
Hyunmin Jeon, Gary Geunbae Lee
Recently, reinforcement learning (RL) has been applied to task-oriented dialogue systems by using latent actions to solve shortcomings of supervised learning (SL). In this paper, w…