<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="prato-etal-2020-fully">
    <titleInfo>
        <title>Fully Quantized Transformer for Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Gabriele</namePart>
        <namePart type="family">Prato</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ella</namePart>
        <namePart type="family">Charlaix</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mehdi</namePart>
        <namePart type="family">Rezagholizadeh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>State-of-the-art neural machine translation methods employ massive amounts of parameters. Drastically reducing computational costs of such methods without affecting performance has been up to this point unsuccessful. To this end, we propose FullyQT: an all-inclusive quantization strategy for the Transformer. To the best of our knowledge, we are the first to show that it is possible to avoid any loss in translation quality with a fully quantized Transformer. Indeed, compared to full-precision, our 8-bit models score greater or equal BLEU on most tasks. Comparing ourselves to all previously proposed methods, we achieve state-of-the-art quantization results.</abstract>
    <identifier type="citekey">prato-etal-2020-fully</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.1</identifier>
    <location>
        <url>https://aclanthology.org/2020.findings-emnlp.1</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>1</start>
            <end>14</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Fully Quantized Transformer for Machine Translation
%A Prato, Gabriele
%A Charlaix, Ella
%A Rezagholizadeh, Mehdi
%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 prato-etal-2020-fully
%X State-of-the-art neural machine translation methods employ massive amounts of parameters. Drastically reducing computational costs of such methods without affecting performance has been up to this point unsuccessful. To this end, we propose FullyQT: an all-inclusive quantization strategy for the Transformer. To the best of our knowledge, we are the first to show that it is possible to avoid any loss in translation quality with a fully quantized Transformer. Indeed, compared to full-precision, our 8-bit models score greater or equal BLEU on most tasks. Comparing ourselves to all previously proposed methods, we achieve state-of-the-art quantization results.
%R 10.18653/v1/2020.findings-emnlp.1
%U https://aclanthology.org/2020.findings-emnlp.1
%U https://doi.org/10.18653/v1/2020.findings-emnlp.1
%P 1-14

Markdown (Informal)

[Fully Quantized Transformer for Machine Translation](https://aclanthology.org/2020.findings-emnlp.1) (Prato et al., Findings 2020)

• Fully Quantized Transformer for Machine Translation (Prato et al., Findings 2020)

ACL

• Gabriele Prato, Ella Charlaix, and Mehdi Rezagholizadeh. 2020. Fully Quantized Transformer for Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1–14, Online. Association for Computational Linguistics.