<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhao-etal-2020-active">
    <titleInfo>
        <title>Active Learning Approaches to Enhancing Neural Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuekai</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Haoran</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shuchang</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhihua</namePart>
        <namePart type="family">Zhang</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>Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget. We design a word frequency based acquisition function and combine it with a strong uncertainty based method. The combined method steadily outperforms all other acquisition functions in various scenarios. As far as we know, we are the first to do a large-scale study on actively training Transformer for NMT. Specifically, with a human translation budget of only 20% of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs.</abstract>
    <identifier type="citekey">zhao-etal-2020-active</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.162</identifier>
    <location>
        <url>https://aclanthology.org/2020.findings-emnlp.162</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>1796</start>
            <end>1806</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Active Learning Approaches to Enhancing Neural Machine Translation
%A Zhao, Yuekai
%A Zhang, Haoran
%A Zhou, Shuchang
%A Zhang, Zhihua
%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 zhao-etal-2020-active
%X Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget. We design a word frequency based acquisition function and combine it with a strong uncertainty based method. The combined method steadily outperforms all other acquisition functions in various scenarios. As far as we know, we are the first to do a large-scale study on actively training Transformer for NMT. Specifically, with a human translation budget of only 20% of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs.
%R 10.18653/v1/2020.findings-emnlp.162
%U https://aclanthology.org/2020.findings-emnlp.162
%U https://doi.org/10.18653/v1/2020.findings-emnlp.162
%P 1796-1806

Markdown (Informal)

[Active Learning Approaches to Enhancing Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.162) (Zhao et al., Findings 2020)

• Active Learning Approaches to Enhancing Neural Machine Translation (Zhao et al., Findings 2020)

ACL

• Yuekai Zhao, Haoran Zhang, Shuchang Zhou, and Zhihua Zhang. 2020. Active Learning Approaches to Enhancing Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1796–1806, Online. Association for Computational Linguistics.