Papers by Chaehyeong Kim

2 papers
BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets (2022.emnlp-main)

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Challenge: a number of largescale datasets targeting a specific conversational skill have recently become available.
Approach: They propose a framework where multiple agents grounded to specific skills participate in a conversation to automatically annotate multi-skill dialogues.
Outcome: The proposed framework can be used to build open-domain chatbots with diverse communicative skills.
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (2022.coling-1)

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Challenge: Existing frameworks that use commonsense as supervision only use input knowledge, but it generates more informative and consistent summaries.
Approach: They propose to leverage the unique characteristics of dialogues sharing commonsense knowledge to solve the difficulties in summarizing them.
Outcome: The proposed framework generates more informative and consistent summaries with injected commonsense knowledge than existing methods.

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