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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>What do you learn from context? Probing for sentence structure in contextualized word representations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1905.06316/</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/1905.06316/feed.xml" rel="self" type="application/rss+xml" /><item><title>What do you learn from context? Probing for sentence structure in contextualized word representations — mentioned by enisberk</title><link>https://news.ycombinator.com/item?id=21351611</link><guid isPermaLink="false">urn:hackerpapers:mention:1905.06316:21351611</guid><pubDate>Fri, 25 Oct 2019 03:29:20 GMT</pubDate><description>&lt;p&gt;I attended one of the talks(1) of the Sam Bowman. His talk was about &amp;quot;Task-Independent Language Understanding&amp;quot; and he also talked about GLUE and super GLUE; he mentioned that some models are passing an average person in experiments. They did some experiments to understand BERT&amp;#x27;s performance (2). (similar to article &amp;#x27;NLP&amp;#x27;s Clever Hans Moment&amp;#x27;) But they found a different answer to question &amp;quot;what BERT really knows,&amp;quot; so he was skeptical about all conclusions. Check these out if you are interested in. (1)[https://www.nyu.edu/projects/bowman/TILU-talk-19-09.pdf] (2)[https://arxiv.org/abs/1905.06316]&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1905.06316/"&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>
