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        <title>Finding the Optimal Vocabulary Size for Neural Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Thamme</namePart>
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            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore its effect on NMT. We analyze the effect of various vocabulary sizes on NMT performance on multiple languages with many data sizes, and reveal an explanation for why certain vocabulary sizes are better than others.</abstract>
    <identifier type="citekey">gowda-may-2020-finding</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.352</identifier>
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%0 Conference Proceedings
%T Finding the Optimal Vocabulary Size for Neural Machine Translation
%A Gowda, Thamme
%A May, Jonathan
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gowda-may-2020-finding
%X We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore its effect on NMT. We analyze the effect of various vocabulary sizes on NMT performance on multiple languages with many data sizes, and reveal an explanation for why certain vocabulary sizes are better than others.
%R 10.18653/v1/2020.findings-emnlp.352
%U https://aclanthology.org/2020.findings-emnlp.352
%U https://doi.org/10.18653/v1/2020.findings-emnlp.352
%P 3955-3964

Markdown (Informal)

[Finding the Optimal Vocabulary Size for Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.352) (Gowda & May, Findings 2020)

• Finding the Optimal Vocabulary Size for Neural Machine Translation (Gowda & May, Findings 2020)

ACL

• Thamme Gowda and Jonathan May. 2020. Finding the Optimal Vocabulary Size for Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3955–3964, Online. Association for Computational Linguistics.