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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Paraphrase Identification with Deep Learning: A Review of Datasets and Methods · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2212.06933/</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/2212.06933/feed.xml" rel="self" type="application/rss+xml" /><item><title>Paraphrase Identification with Deep Learning: A Review of Datasets and Methods — mentioned by burkaman</title><link>https://news.ycombinator.com/item?id=46721621</link><guid isPermaLink="false">urn:hackerpapers:mention:2212.06933:46721621</guid><pubDate>Thu, 22 Jan 2026 16:39:57 GMT</pubDate><description>&lt;p&gt;If you click on the article you can see a full list of the hallucinations they found. They did put in the effort to look for plausible partial matches, but most of them are some variation of &amp;quot;No author or title match. Doesn&amp;#x27;t exist in publication.&amp;quot; Here&amp;#x27;s a random one I picked as an example. Paper: https://openreview.net/pdf?id=IiEtQPGVyV Reference: Asma Issa, George Mohler, and John Johnson. Paraphrase identification using deep contextual- ized representations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 517–526, 2018. Asma Issa and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2212.06933/"&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>
