activity
20212025
most citedGenerating Personalized Dialogue via Multi-Task Meta-Learning

4 citations · 5 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CL2025

DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities

Jing Yang Lee, Hamed Bonab, Nasser Zalmout +6

Large Language Models (LLMs) are increasingly employed in multi-turn conversational tasks, yet their pre-training data predominantly consists of continuous prose, creating a potent…

cs.CL2025

Modeling the One-to-Many Property in Open-Domain Dialogue with LLMs

Jing Yang Lee, Kong-Aik Lee, Woon-Seng Gan

Open-domain Dialogue (OD) exhibits a one-to-many (o2m) property, whereby multiple appropriate responses exist for a single dialogue context. Despite prior research showing that mod…

cs.CL2024

Redefining Proactivity for Information Seeking Dialogue

Jing Yang Lee, Seokhwan Kim, Kartik Mehta +3

Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in…

cs.CL2023

An Empirical Bayes Framework for Open-Domain Dialogue Generation

Jing Yang Lee, Kong Aik Lee, Woon-Seng Gan

To engage human users in meaningful conversation, open-domain dialogue agents are required to generate diverse and contextually coherent dialogue. Despite recent advancements, whic…

cs.CL20231 cited

Partially Randomizing Transformer Weights for Dialogue Response Diversity

Jing Yang Lee, Kong Aik Lee, Woon-Seng Gan

Despite recent progress in generative open-domain dialogue, the issue of low response diversity persists. Prior works have addressed this issue via either novel objective functions…

cs.CL2022

Improving Contextual Coherence in Variational Personalized and Empathetic Dialogue Agents

Jing Yang Lee, Kong Aik Lee, Woon Seng Gan

In recent years, latent variable models, such as the Conditional Variational Auto Encoder (CVAE), have been applied to both personalized and empathetic dialogue generation. Prior w…