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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2010.06775/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2010.06775/feed.xml" rel="self" type="application/rss+xml" /><item><title>Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision — mentioned by deeplstm</title><link>https://news.ycombinator.com/item?id=24858452</link><guid isPermaLink="false">urn:hackerpapers:mention:2010.06775:24858452</guid><pubDate>Thu, 22 Oct 2020 14:35:42 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s a super cool paper that invents &amp;quot;vokenization&amp;quot; to generate a large amount of visually-grounded language datasets and trains visually-grounded models on those. Most language models are trained on pure text data. Although it achieves significant success in recent years, this is not how humans acquire a language. It raises an interesting question &amp;quot;Can language models achieve a high level of language understanding by reading the text input alone?&amp;quot; The answer is probably &amp;quot;no&amp;quot;. To push the boundary of language models, adding other learning signals in the learning process is the key to success.…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2010.06775/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
