<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ul-haq-etal-2020-improving">
    <titleInfo>
        <title>Improving Document-Level Neural Machine Translation with Domain Adaptation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sami</namePart>
        <namePart type="family">Ul Haq</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sadaf</namePart>
        <namePart type="family">Abdul Rauf</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Arslan</namePart>
        <namePart type="family">Shoukat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Noor-e-</namePart>
        <namePart type="family">Hira</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourth Workshop on Neural Generation and Translation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Birch</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andrew</namePart>
            <namePart type="family">Finch</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hiroaki</namePart>
            <namePart type="family">Hayashi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kenneth</namePart>
            <namePart type="family">Heafield</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marcin</namePart>
            <namePart type="family">Junczys-Dowmunt</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ioannis</namePart>
            <namePart type="family">Konstas</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xian</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Graham</namePart>
            <namePart type="family">Neubig</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Oda</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>Recent studies have shown that translation quality of NMT systems can be improved by providing document-level contextual information. In general sentence-based NMT models are extended to capture contextual information from large-scale document-level corpora which are difficult to acquire. Domain adaptation on the other hand promises adapting components of already developed systems by exploiting limited in-domain data. This paper presents FJWU’s system submission at WNGT, we specifically participated in Document level MT task for German-English translation. Our system is based on context-aware Transformer model developed on top of original NMT architecture by integrating contextual information using attention networks. Our experimental results show providing previous sentences as context significantly improves the BLEU score as compared to a strong NMT baseline. We also studied the impact of domain adaptation on document level translationand were able to improve results by adaptingthe systems according to the testing domain.</abstract>
    <identifier type="citekey">ul-haq-etal-2020-improving</identifier>
    <identifier type="doi">10.18653/v1/2020.ngt-1.27</identifier>
    <location>
        <url>https://aclanthology.org/2020.ngt-1.27</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>225</start>
            <end>231</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Improving Document-Level Neural Machine Translation with Domain Adaptation
%A Ul Haq, Sami
%A Abdul Rauf, Sadaf
%A Shoukat, Arslan
%A Hira, Noor-e-
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Hayashi, Hiroaki
%Y Heafield, Kenneth
%Y Junczys-Dowmunt, Marcin
%Y Konstas, Ioannis
%Y Li, Xian
%Y Neubig, Graham
%Y Oda, Yusuke
%S Proceedings of the Fourth Workshop on Neural Generation and Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ul-haq-etal-2020-improving
%X Recent studies have shown that translation quality of NMT systems can be improved by providing document-level contextual information. In general sentence-based NMT models are extended to capture contextual information from large-scale document-level corpora which are difficult to acquire. Domain adaptation on the other hand promises adapting components of already developed systems by exploiting limited in-domain data. This paper presents FJWU’s system submission at WNGT, we specifically participated in Document level MT task for German-English translation. Our system is based on context-aware Transformer model developed on top of original NMT architecture by integrating contextual information using attention networks. Our experimental results show providing previous sentences as context significantly improves the BLEU score as compared to a strong NMT baseline. We also studied the impact of domain adaptation on document level translationand were able to improve results by adaptingthe systems according to the testing domain.
%R 10.18653/v1/2020.ngt-1.27
%U https://aclanthology.org/2020.ngt-1.27
%U https://doi.org/10.18653/v1/2020.ngt-1.27
%P 225-231

Markdown (Informal)

[Improving Document-Level Neural Machine Translation with Domain Adaptation](https://aclanthology.org/2020.ngt-1.27) (Ul Haq et al., NGT 2020)

• Improving Document-Level Neural Machine Translation with Domain Adaptation (Ul Haq et al., NGT 2020)

ACL

• Sami Ul Haq, Sadaf Abdul Rauf, Arslan Shoukat, and Noor-e- Hira. 2020. Improving Document-Level Neural Machine Translation with Domain Adaptation. In Proceedings of the Fourth Workshop on Neural Generation and Translation, pages 225–231, Online. Association for Computational Linguistics.