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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Unsupervised Pretraining for Sequence to Sequence Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.02683/</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/1611.02683/feed.xml" rel="self" type="application/rss+xml" /><item><title>Unsupervised Pretraining for Sequence to Sequence Learning — mentioned by prajit</title><link>https://news.ycombinator.com/item?id=12991913</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.02683:12991913</guid><pubDate>Sat, 19 Nov 2016 03:29:14 GMT</pubDate><description>&lt;p&gt;If you&amp;#x27;re interested in Sequence to Sequence tasks (e.g. neural machine translation or abstractive summarization) with small data, check out our recent paper from Google Brain tackling this problem (disclaimer, I&amp;#x27;m the first author): https://arxiv.org/abs/1611.02683&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.02683/"&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>
