Joongwon (Daniel) Kim

I am an incoming Ph.D. student in the Natural Language Processing group at the University of Washington. I am thankful to be advised by Hannaneh Hajishirzi. Previously, I was an undergrad at the University of Pennsylvania, working with Chris Callison-Burch and Mark Yatskar.

My research interests lie in natural language processing and machine learning, currently in building systems that address information needs with limited inputs and resources.

News

  • 09/2022: I begin my Ph.D. at the University of Washington!
  • 04/2022: I have been awarded the NSF-GRFP Fellowship (2022-27).
  • 03/2022: I have been awarded the CSE Educators' Endowed Fellowship in Computer Science & Engineering from the Allen School.
  • 12/2021: I have been selected for honorable mentions for the CRA Outstanding Undergraduate Researcher Awards 2022.

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Publications / Pre-Prints
Induce, Edit, Retrieve: Language Grounded Multimodal Schema for Instructional Video Retrieval
Yue Yang, Joongwon Kim, Artemis Panagopolou, Mark Yatskar, Chris Callison-Burch
arXiv (Under Review)  
Paper

We built schemas for goal-oriented tasks by aligning YouTube videos with wikiHow steps. Then, we proposed methods for editing the schemas to handle unseen but related tasks. Finally, we leveraged our schemas to perform instructional video retrieval on several datasets and demonstrated that our method improves over other retrieval approaches.

BiSECT: Learning to Split and Rephrase Sentences with Bitexts
Joongwon Kim*, Mounica Maddela*, Reno Kriz, Wei Xu, Chris Callison-Burch
Proceedings of EMNLP, 2021 (long)  
Paper  |  Code  |  Video  |  Poster

We curated a multilingual corpus for sentence splitting by using machine translation over parallel corpora. Moreover, we developed a sentence splitter with controllable generation. We showed that our dataset and model outperformed existing methods in both automatic and human evaluations. Work done in collaboration with Georgia Tech.

Projects
Conversational QA with Synthesized Dialogues
Joongwon Kim, Mark Yatskar, Chris Callison-Burch
Work in progress  

I proposed a new method to collect series of related questions for a given topic by utilizing People-Also-Ask data. Currently, I am investigating approaches to return answers for questions in my dataset by incorporating the context articles.


Website source from Jon Barron