November 2, 2019 Conference paper Open Access

Nguyen, Toan Q.; Salazar, Julian

DCAT Export

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        <foaf:name>Nguyen, Toan Q.</foaf:name>
        <foaf:givenName>Toan Q.</foaf:givenName>
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            <foaf:name>University of Notre Dame</foaf:name>
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        <foaf:name>Salazar, Julian</foaf:name>
        <foaf:givenName>Julian</foaf:givenName>
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    <dct:title>Transformers without Tears: Improving the Normalization of Self-Attention</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-11-02</dct:issued>
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    <dct:description>&lt;p&gt;We evaluate three simple, normalization-centric changes to improve Transformer training. First, we show that pre-norm residual connections (PRENORM) and smaller initializations enable warmup-free, validation-based training with large learning rates. Second, we propose&amp;nbsp;l2&amp;nbsp;normalization with a single scale parameter (SCALENORM) for faster training and better performance. Finally, we reaffirm the effectiveness of normalizing word embeddings to a fixed length (FIXNORM). On five low-resource translation pairs from TED Talks-based corpora, these changes always converge, giving an average +1.1 BLEU over state-of-the-art bilingual baselines and a new 32.8 BLEU on IWSLT &amp;#39;15 English-Vietnamese. We ob- serve sharper performance curves, more consistent gradient norms, and a linear relationship between activation scaling and decoder depth. Surprisingly, in the high-resource setting (WMT &amp;#39;14 English-German), SCALENORM&amp;nbsp;and FIXNORM&amp;nbsp;remain competitive but PRENORM&amp;nbsp;degrades performance.&lt;/p&gt;</dct:description>
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