<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Masked-Language-Model on</title><link>https://h30306.github.io/blog/tags/masked-language-model/</link><description>Recent content in Masked-Language-Model on</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>hwork0511@gmail.com (Wei Yi Chung)</managingEditor><webMaster>hwork0511@gmail.com (Wei Yi Chung)</webMaster><copyright>© 2026 Wei Yi Chung</copyright><lastBuildDate>Tue, 22 Mar 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://h30306.github.io/blog/tags/masked-language-model/index.xml" rel="self" type="application/rss+xml"/><item><title>DVA-BERT: Sentiment-Based Masked Language Modeling for Valence–Arousal Prediction</title><link>https://h30306.github.io/blog/paper/2022-applied-intelligence-dva-bert/</link><pubDate>Tue, 22 Mar 2022 00:00:00 +0000</pubDate><author>hwork0511@gmail.com (Wei Yi Chung)</author><guid>https://h30306.github.io/blog/paper/2022-applied-intelligence-dva-bert/</guid><description>Applied Intelligence 2022: masking sentiment words by their intensity instead of random tokens, as an auxiliary task that improves sentence-level valence–arousal prediction</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://h30306.github.io/blog/paper/2022-applied-intelligence-dva-bert/featured.png"/></item></channel></rss>