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<h3 class="text-body mx-auto mt-2">Jongin Kim</h3>
<h5>PhD in Computer Science, Boston University</h5>
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<h1>Jongin Kim</h1>
I am a first-year PhD student in Computer Science at Boston University, where I am advised by Prof. Derry Wijaya.<br><br>
My research interest is Multilingual/Crosslingual NLP.<br>
My goal is to develop NLP systems that work well across different languages, so that langauge technologies are easily accessible to diverse and/or disadvantaged users who may need them most.<br><br>
I have done some <a href='#research' target="_self">research</a> towards this research direction. (<a href='#pubs' target="_self">publications</a>)<br>
Also, I plan to further <a href='#researchDirection' target="_self">broaden my research topics</a> in the future while I pursue my Ph.D. !
</section>
<section class="about-section" id="pubs">
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<h1>Publications</h1>
<h4>2021</h4>
<ul class="clist">
<li>
<a href="https://aclanthology.org/2021.emnlp-main.405/">Analysis of Zero-Shot Crosslingual Learning between English and Korean
for Named Entity Recognition</a>
<br>
<b>Jongin Kim</b>, Nayoung Choi, Seunghyun S. Lim, Jungwhan Kim, Soojin Chung, Hyunsoo Woo, Min Song, and Jinho D. Choi
<br>
<i>In Proceedings of the EMNLP Workshop on Multilingual Representation Learning (MRL), 2021</i>
<br>
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<a href="https://aclanthology.org/2021.mrl-1.19.pdf"><span class="badge badge-pdf"> Paper </span></a>
<a href="https://drive.google.com/file/d/1gXfCTuMvM3aaRtpNsj6GRJrXq0W7Wgs1/view"><span class="badge badge-video"> Presentation </span></a>
<!-- <div id="abs8" class="popup-abs">
Coreference resolution is key to many natural language processing tasks and yet has only been explored for spoken languages.
In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language.
In this paper, we: (1) introduce Signed Coreference Resolution, a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively. Finally, we invite the NLP community to collaborate with signing communities and direct efforts towards SCR to close this gap.
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<li>
<a href="https://aclanthology.org/2021.emnlp-main.553/">FantasyCoref: Coreference Resolution on Fantasy Literature Through Omniscient Writer’s Point of View</a>
<br>
Sooyoun Han, Sumin Seo, Minji Kang, <b>Jongin Kim</b>, Nayoung Choi, Min Song, and Jinho D. Choi
<br>
<i>In Proceedings of the EMNLP Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC), 2021</i>
<br>
<a href="https://aclanthology.org/2021.crac-1.3/"><span class="badge badge-secondary"> Anthology </span></a>
<a href="https://aclanthology.org/2021.crac-1.3.pdf"><span class="badge badge-pdf"> Paper </span></a>
<a href="https://drive.google.com/file/d/1bqhqCZzUFT9t_3E0p2-rl7pM4dg3z5th/view"><span class="badge badge-video"> Presentation </span></a>
</section>
<section class="about-section" id="researchDirection">
<div class="text-start">
<h1>Future Research Directions</h1>
<ol>
<!-- 1 -->
<li>Efficient Way of Building Dataset for Low-Resource languages</li>
<ul>
<li>Ways to efficiently build monolingual labeled dataset for low-resource languages<br>(Particularly, data that contain knowledge that cannot be transferred from other high-resoure languages)</li>
<ul>
<li>Active Learning, Human-in-the-loop machine learning</li>
</ul>
</ul>
<!-- 2 -->
<li>Ways to efficiently crowdsource/collect Parallel texts<br>
(for Multilingual Neural Machine Translation)</li>
<ul>
<li>Annotation using Image or GIFs as pivots</li>
<li>Collect Parallel(Comparable) texts from Web and Filter out noise</li>
</ul>
<!-- 3 -->
<li>Multilingual Benchmark Datasets that enable more comprehensive evaluation of Multilingual Models</li>
<ul>
<li>Inclusion of typologically diverse languages</li>
<li>Inclusion of more challenging NLU/NLG tasks</li>
</ul>
<!-- 4 -->
<li>Novel Methods for Pre-Training Multilingual Language Models for more accurate alignment accross different languages (for better multilingual representations)</li>
<ul>
<li>Multilingual subword tokenizer</li>
<li>Encourage explicit attention between languages</li>
<li>Augmenting the model with linguistic knowledge<br>
(= incorporating linguistic knowledge into the model)</li>
</ul>
<!-- 5 -->
<li>Explore Ways to improve Cross-Lingual Transfer Learning</li>
<ul>
<li>Improving zero-shot cross-lingual transfer (Direct Model Transfer)</li>
<ul>
<li>Intermediate task training</li>
</ul>
<li>Overcoming Word Order Difference</li>
<li>Annotation Projection</li>
</ul>
<!-- 6 -->
<li>Applying Cross-Lingual Transfer Learning to Other Tasks<br>
(Would like to explore ways to apply Cross-lingual Transfer Learning to more challenging Tasks)</li>
<ul>
<li>Crossingual IR, including QA, Text Summarization</li>
</ul>
<!-- 7 -->
<li>Multilingual Neural Machine Translation</li>
</section>
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<p style="color:#808080; font-size:16px;" align="center">Copyright © Jongin Kim 2021
<br> Last updated December 20 2021
</p>
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