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    <title>Deep Learning on Jason Lunder</title>
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      <title>Tree Matching Networks: What If We Gave Neural Networks the Parse Tree Instead?</title>
      <link>https://jasonlunder.com/post/tree-matching-networks/</link>
      <pubDate>Sun, 08 Feb 2026 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;When we feed text to a transformer, the model has to figure out the structure of language entirely from data. Subject-verb relationships, modifier scope, negation boundaries: all of it must be learned implicitly from millions (or billions) of examples. But what if we just&amp;hellip; told the model the structure upfront?&lt;/p&gt;&#xA;&lt;p&gt;That&amp;rsquo;s the core idea behind &lt;a href=&#34;https://arxiv.org/abs/2512.00204&#34;&gt;Tree Matching Networks&lt;/a&gt; (TMN), my recent research exploring whether dependency parse trees can serve as an efficient structural prior for natural language inference. The short version: a graph neural network operating on parse trees achieved 75.20% accuracy on the SNLI benchmark versus 35.38% for a BERT baseline of comparable size, both trained from scratch on the same data and the same hardware.&lt;/p&gt;</description>
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