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<p class="kicker">10 &middot; RESULTS</p>
<h1>Beyond Translation: Constituency Parsing</h1>
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<li>A <b>4-layer Transformer</b> was trained on English constituency parsing (Wall Street Journal portion of the Penn Treebank, ~40K training sentences).</li>
<li>With almost <b>no task-specific tuning</b>, it outperforms the BerkeleyParser &mdash; even when trained only on the WSJ training set.</li>
<li>In the <b>semi-supervised</b> setting (with a ~17M-sentence high-confidence corpus) it beats all previously reported models <b>except the Recurrent Neural Network Grammar</b>.</li>
<li>The same architecture generalizes across tasks without structural changes.</li>
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<p class="bignum">91.3 F1</p>
<p class="statlab">WSJ-only, discriminative setting (Section 23 of WSJ)</p>
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<p class="bignum">92.7 F1</p>
<p class="statlab">Semi-supervised setting &mdash; above every prior model except RNNG</p>
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<p class="foot">Source: Table 4 (paper p. 10) and &sect;6.2 / &ldquo;English Constituency Parsing&rdquo; (paper p. 9&ndash;10).</p>
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