Papers by Jihyoung Jang

3 papers
Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations (2023.emnlp-main)

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Challenge: open-domain chatbots focus on short single-session dialogue, neglecting the potential need for understanding contextual information in multiple consecutive sessions.
Approach: They propose a 1M multi-session dialogue dataset for integrating time intervals and speaker relationships into a long-term conversation setup.
Outcome: The proposed model can generate coherent responses according to time intervals and speaker relationships with high user engagement without contradiction in a long-term conversation setup.
Enabling Chatbots with Eyes and Ears: An Immersive Multimodal Conversation System for Dynamic Interactions (2025.acl-long)

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Challenge: Multimodality has been explored in multi-party and multi-session conversations, but task-specific constraints have hindered its seamless integration into dynamic, natural conversations.
Approach: They propose a multimodal conversation dataset and a model with multimodal memory retrieval to equip chatbots with "eyes and ears" they aim to integrate multimodality into chatbot interactions by integrating visual and auditory inputs into the chatbot.
Outcome: The proposed model demonstrates the ability to engage in long-term conversations with multiple speakers in complex, real-world-like settings, effectively processing visual and auditory inputs to understand and respond appropriately.
Mixed-Session Conversation with Egocentric Memory (2024.findings-emnlp)

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Challenge: Recent dialogue systems exhibit an inability to replicate dynamic, continuous, long-term interactions involving multiple partners.
Approach: They propose a multi-session dialogue system that builds on real-world interactions by integrating deep layered interactions and widening conversation networks.
Outcome: The proposed system is based on a dataset of 6 consecutive dialogue episodes with four speakers (one main speaker and three partners) appearing in each episode.

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