Search Results for author: Sungdong Kim

Found 12 papers, 6 papers with code

Leveraging Pre-Trained Language Models to Streamline Natural Language Interaction for Self-Tracking

no code implementations31 May 2022 Young-Ho Kim, Sungdong Kim, Minsuk Chang, Sang-Woo Lee

Current natural language interaction for self-tracking tools largely depends on bespoke implementation optimized for a specific tracking theme and data format, which is neither generalizable nor scalable to a tremendous design space of self-tracking.

Towards More Realistic Generation of Information-Seeking Conversations

no code implementations25 May 2022 Gangwoo Kim, Sungdong Kim, Kang Min Yoo, Jaewoo Kang

In this paper, we introduce a novel framework SimSeek (simulating information-seeking conversation from unlabeled documents) and compare two variants of it to provide a deeper perspective into the information-seeking behavior.

Conversational Search

Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models

1 code implementation NAACL 2022 Sanghwan Bae, Donghyun Kwak, Sungdong Kim, Donghoon Ham, Soyoung Kang, Sang-Woo Lee, WooMyoung Park

In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans.

Few-Shot Learning

Saving Dense Retriever from Shortcut Dependency in Conversational Search

no code implementations15 Feb 2022 Sungdong Kim, Gangwoo Kim

To prevent models from solely relying on the shortcut, we explore iterative hard negatives mined by pre-trained dense retrievers.

Conversational Search

Can Language Models be Biomedical Knowledge Bases?

1 code implementation EMNLP 2021 Mujeen Sung, Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, Jaewoo Kang

To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs.

Efficient Dialogue State Tracking by Selectively Overwriting Memory

3 code implementations ACL 2020 Sungdong Kim, Sohee Yang, Gyuwan Kim, Sang-Woo Lee

This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations.

Dialogue State Tracking Multi-domain Dialogue State Tracking

QADiver: Interactive Framework for Diagnosing QA Models

no code implementations1 Dec 2018 Gyeongbok Lee, Sungdong Kim, Seung-won Hwang

Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset.

Question Answering

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