221 citations · 230 across the 6 of their papers we have counts for
6 papers
ReIn: Conversational Error Recovery with Reasoning Inception
Takyoung Kim, Jinseok Nam, Chandrayee Basu +5
Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to una…
Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests
Amogh Mannekote, Jinseok Nam, Ziming Li +3
Indirect User Requests (IURs), such as "It's cold in here" instead of "Could you please increase the temperature?" are common in human-human task-oriented dialogue and require worl…
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems
Mohammad Kachuee, Jinseok Nam, Sarthak Ahuja +2
Skill routing is an important component in large-scale conversational systems. In contrast to traditional rule-based skill routing, state-of-the-art systems use a model-based appro…
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration
Han Li, Sunghyun Park, Aswarth Dara +5
Current state-of-the-art large-scale conversational AI or intelligent digital assistant systems in industry comprises a set of components such as Automatic Speech Recognition (ASR)…
On Learning Vector Representations in Hierarchical Label Spaces
Jinseok Nam, Johannes Fürnkranz
An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem…
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Jinseok Nam, Jungi Kim, Eneldo Loza Mencía +2
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we inves…