@inproceedings{eger-etal-2018-pd3,
title = "{PD}3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection",
author = {Eger, Steffen and
R{\"u}ckl{\'e}, Andreas and
Gurevych, Iryna},
editor = "Slonim, Noam and
Aharonov, Ranit",
booktitle = "Proceedings of the 5th Workshop on Argument Mining",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5216",
doi = "10.18653/v1/W18-5216",
pages = "131--143",
abstract = "We consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging. We combine direct transfer using bilingual embeddings with annotation projection, which projects labels across unlabeled parallel data. We do so by either merging respective source and target language datasets or alternatively by using multi-task learning. Our combination strategy considerably improves upon both direct transfer and projection with few available parallel sentences, the most realistic scenario for many low-resource target languages.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="eger-etal-2018-pd3">
<titleInfo>
<title>PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Steffen</namePart>
<namePart type="family">Eger</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andreas</namePart>
<namePart type="family">Rücklé</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iryna</namePart>
<namePart type="family">Gurevych</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th Workshop on Argument Mining</title>
</titleInfo>
<name type="personal">
<namePart type="given">Noam</namePart>
<namePart type="family">Slonim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ranit</namePart>
<namePart type="family">Aharonov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Brussels, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging. We combine direct transfer using bilingual embeddings with annotation projection, which projects labels across unlabeled parallel data. We do so by either merging respective source and target language datasets or alternatively by using multi-task learning. Our combination strategy considerably improves upon both direct transfer and projection with few available parallel sentences, the most realistic scenario for many low-resource target languages.</abstract>
<identifier type="citekey">eger-etal-2018-pd3</identifier>
<identifier type="doi">10.18653/v1/W18-5216</identifier>
<location>
<url>https://aclanthology.org/W18-5216</url>
</location>
<part>
<date>2018-11</date>
<extent unit="page">
<start>131</start>
<end>143</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection
%A Eger, Steffen
%A Rücklé, Andreas
%A Gurevych, Iryna
%Y Slonim, Noam
%Y Aharonov, Ranit
%S Proceedings of the 5th Workshop on Argument Mining
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F eger-etal-2018-pd3
%X We consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging. We combine direct transfer using bilingual embeddings with annotation projection, which projects labels across unlabeled parallel data. We do so by either merging respective source and target language datasets or alternatively by using multi-task learning. Our combination strategy considerably improves upon both direct transfer and projection with few available parallel sentences, the most realistic scenario for many low-resource target languages.
%R 10.18653/v1/W18-5216
%U https://aclanthology.org/W18-5216
%U https://doi.org/10.18653/v1/W18-5216
%P 131-143
Markdown (Informal)
[PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection](https://aclanthology.org/W18-5216) (Eger et al., ArgMining 2018)
ACL