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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LSTM-based Deep Learning Models for Non-factoid Answer Selection · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.04108/</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/1511.04108/feed.xml" rel="self" type="application/rss+xml" /><item><title>LSTM-based Deep Learning Models for Non-factoid Answer Selection — mentioned by nl</title><link>https://news.ycombinator.com/item?id=10726085</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.04108:10726085</guid><pubDate>Sun, 13 Dec 2015 11:34:42 GMT</pubDate><description>&lt;p&gt;BTW, your CNN for NLP post is interesting too. You might find LSTM-BASED DEEP LEARNING MODELS FOR NON- FACTOID ANSWER SELECTION[1] (from the IBM Watson team) interesting. They combine a CNN with a LSTM for question answering on complex, non-factoid questions. Their LSTM+Attention model performs slightly better, but it&amp;#x27;s a pretty interesting approach. [1] http://arxiv.org/abs/1511.04108&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.04108/"&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>
