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‎feed.xml‎

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<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="http://localhost:4000/feed.xml" rel="self" type="application/atom+xml" /><link href="http://localhost:4000/" rel="alternate" type="text/html" /><updated>2025-04-28T22:09:48+09:00</updated><id>http://localhost:4000/feed.xml</id><entry><title type="html">Welcome to Jekyll!</title><link href="http://localhost:4000/jekyll/update/2024/02/22/welcome-to-jekyll.html" rel="alternate" type="text/html" title="Welcome to Jekyll!" /><published>2024-02-22T23:03:29+09:00</published><updated>2024-02-22T23:03:29+09:00</updated><id>http://localhost:4000/jekyll/update/2024/02/22/welcome-to-jekyll</id><content type="html" xml:base="http://localhost:4000/jekyll/update/2024/02/22/welcome-to-jekyll.html"><![CDATA[<p>You’ll find this post in your <code class="language-plaintext highlighter-rouge">_posts</code> directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run <code class="language-plaintext highlighter-rouge">jekyll serve</code>, which launches a web server and auto-regenerates your site when a file is updated.</p>
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<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="http://localhost:4000/feed.xml" rel="self" type="application/atom+xml" /><link href="http://localhost:4000/" rel="alternate" type="text/html" /><updated>2025-11-12T12:50:32+09:00</updated><id>http://localhost:4000/feed.xml</id><entry><title type="html">Welcome to Jekyll!</title><link href="http://localhost:4000/jekyll/update/2024/02/22/welcome-to-jekyll.html" rel="alternate" type="text/html" title="Welcome to Jekyll!" /><published>2024-02-22T23:03:29+09:00</published><updated>2024-02-22T23:03:29+09:00</updated><id>http://localhost:4000/jekyll/update/2024/02/22/welcome-to-jekyll</id><content type="html" xml:base="http://localhost:4000/jekyll/update/2024/02/22/welcome-to-jekyll.html"><![CDATA[<p>You’ll find this post in your <code class="language-plaintext highlighter-rouge">_posts</code> directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run <code class="language-plaintext highlighter-rouge">jekyll serve</code>, which launches a web server and auto-regenerates your site when a file is updated.</p>
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<p>Jekyll requires blog post files to be named according to the following format:</p>
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‎index.html‎

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}
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</style>
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<link rel="stylesheet" href="/thirdparties/academicons-1.9.4/css/academicons.min.css"/>
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<!-- <link rel="stylesheet" href="/thirdparties/academicons-1.9.4/css/academicons.min.css"/> -->
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<!-- Google tag (gtag.js) -->
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<script async src="https://www.googletagmanager.com/gtag/js?id=G-BW2YSBXLML"></script>
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<script>
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window.dataLayer = window.dataLayer || [];
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function gtag(){dataLayer.push(arguments);}
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gtag('js', new Date());
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gtag('config', 'G-BW2YSBXLML');
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</script>
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<article class="post">
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<h3 id="activities">Activities</h3>
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<ul>
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<li>Reviewer: ACL Rolling Review, LREC-COLING, ICON, IEEE Access</li>
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<li>Emergency Area Chair: ACL Rolling Review</li>
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<li>Reviewer: ARR, TALLIP, LREC-COLING, Springer, ICON, and IEEE Access</li>
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<li>Area Chair: ARR</li>
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</div>

‎publication.html‎

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<div class="post-content">
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<p><a href="https://scholar.google.com/citations?user=zJ0wGWoAAAAJ&amp;hl=en">Google Scholar</a> | <a href="https://dblp.org/pid/254/9654">DBLP</a> | <a href="https://www.semanticscholar.org/author/Hour-Kaing/1453725225">Semantic Scholar</a></p>
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<h1 id="journal">Journal</h1>
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<h1 id="journal-peer-reviewed">Journal (Peer-Reviewed)</h1>
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<ul class="bibliography"><li><span id="mao2025data">Mao, J., Ding, C., <b>Kaing, H.</b>, Tanaka, H., Utiyama, M., &amp; Matsumoto, T. (2025). Data Augmentation for Low-Resource Languages in Multilingual Dependency Parsing. <i>Journal of Natural Language Processing</i>, <i>32</i>(1), 219–251.</span>
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<h1 id="international-conference-peer-reviewed">International Conference (Peer-Reviewed)</h1>
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<ul class="bibliography"><li><span id="raj2025how">Dabre, R., <b>Kaing, H.</b>, &amp; Song, H. (2025). BYTF: How Good Are Byte Level N-Gram F-Scores for Automatic Machine Translation Evaluation? <i>MT Summit</i>.</span>
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<ul class="bibliography"><li><span id="hour2025imagetra"><b>Kaing, H.</b>, Mao, J., Song, H., Ding, C., Tanaka, H., &amp; Utiyama, M. (2025). ImageTra: Real-Time Translation for Texts in Image and Video. <i>Proceedings of the International Joint Conference on Natural Language Processing &amp; Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL)</i>.</span>
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<li><span id="tran2025exploiting">Tran, V.-H., Dabre, R., <b>Kaing, H.</b>, Song, H., Tanaka, H., &amp; Utiyama, M. (2025). Exploiting Word Sense Disambiguation in Large Language Models for Machine Translation. <i>Proceedings of the First Workshop on Language Models for Low-Resource Languages</i>, 135–144.</span>
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<li><span id="song2025structured">Song, H., Eschbach-Dymanus, J., <b>Kaing, H.</b>, Honda, S., Tanaka, H., Buschbeck, B., &amp; Utiyama, M. (2025). Structured Document Translation via Format Reinforcement Learning. <i>Proceedings of the International Joint Conference on Natural Language Processing &amp; Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL)</i>.</span>
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[<a href="https://aclanthology.org/2025.loreslm-1.10/">Paper</a>]
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<li><span id="kaing2025prahokbart"><b>Kaing, H.</b>, Dabre, R., Song, H., Tran, V.-H., Tanaka, H., &amp; Utiyama, M. (2025). PrahokBART: A Pre-trained Sequence-to-Sequence Model for Khmer Natural Language Generation. <i>Proceedings of the 31st International Conference on Computational Linguistics</i>, 1309–1322.</span>
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<li><span id="raj2025how">Dabre, R., <b>Kaing, H.</b>, &amp; Song, H. (2025). BYTF: How Good Are Byte Level N-Gram F-Scores for Automatic Machine Translation Evaluation? <i>MT Summit</i>, 378–387.</span>
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[<a href="https://aclanthology.org/2025.coling-main.87/">Paper</a>]
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[<a href="https://aclanthology.org/2025.mtsummit-1.29">Paper</a>]
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<li><span id="joshi2024connecting">Joshi, A., Kanojia, D., Lent, H., <b>Kaing, H.</b>, &amp; Song, H. (2025). Connecting Ideas in’Lower-Resource’Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios. <i>Proceedings of the 2025 International Conference on Computational Linguistics (COLING 2025)</i>.</span>
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[<a href="https://arxiv.org/abs/2409.12683">Paper</a>]
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<li><span id="tran2025exploiting">Tran, V.-H., Dabre, R., <b>Kaing, H.</b>, Song, H., Tanaka, H., &amp; Utiyama, M. (2025). Exploiting Word Sense Disambiguation in Large Language Models for Machine Translation. <i>Proceedings of the First Workshop on Language Models for Low-Resource Languages</i>, 135–144.</span>
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[<a href="https://aclanthology.org/2025.loreslm-1.10/">Paper</a>]
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[<a href="https://github.com/surrey-nlp/COLING-Tutorial-LowResScene-2025">Slides</a>]
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<li><span id="mao2024overcoming">Mao, J., Ding, C., <b>Kaing, H.</b>, Tanaka, H., Utiyama, M., &amp; Matsumoto, T. (2024). Overcoming Early Saturation on Low-Resource Languages in Multilingual Dependency Parsing. <i>Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD)@ LREC-COLING 2024</i>, 63–69.</span>
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<li><span id="kaing2025prahokbart"><b>Kaing, H.</b>, Dabre, R., Song, H., Tran, V.-H., Tanaka, H., &amp; Utiyama, M. (2025). PrahokBART: A Pre-trained Sequence-to-Sequence Model for Khmer Natural Language Generation. <i>Proceedings of the 31st International Conference on Computational Linguistics</i>, 1309–1322.</span>
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[<a href="https://aclanthology.org/2024.mwe-1.10/">Paper</a>]
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[<a href="https://aclanthology.org/2025.coling-main.87/">Paper</a>]
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<li><span id="linguistic-mt24">Song, H., <b>Kaing, H.</b>, &amp; Dabre, R. (2024). Linguistically Motivated Neural Machine Translation. <i>The 25th Annual Conference of the European Association for Machine Translation (EAMT 2024)</i>.</span>
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<li><span id="mao2024overcoming">Mao, J., Ding, C., <b>Kaing, H.</b>, Tanaka, H., Utiyama, M., &amp; Matsumoto, T. (2024). Overcoming Early Saturation on Low-Resource Languages in Multilingual Dependency Parsing. <i>Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD)@ LREC-COLING 2024</i>, 63–69.</span>
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[<a href="https://aclanthology.org/2024.mwe-1.10/">Paper</a>]
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<h1 id="tutorial">Tutorial</h1>
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<ul class="bibliography"><li><span id="joshi2024connecting">Joshi, A., Kanojia, D., Lent, H., <b>Kaing, H.</b>, &amp; Song, H. (2025). Connecting Ideas in’Lower-Resource’Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios. <i>Proceedings of the 2025 International Conference on Computational Linguistics (COLING 2025)</i>.</span>
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<li><span id="linguistic-mt24">Song, H., <b>Kaing, H.</b>, &amp; Dabre, R. (2024). Linguistically Motivated Neural Machine Translation. <i>The 25th Annual Conference of the European Association for Machine Translation (EAMT 2024)</i>.</span>
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