@inproceedings{jia-zhang-2020-multi,
title = "Multi-Cell Compositional {LSTM} for {NER} Domain Adaptation",
author = "Jia, Chen and
Zhang, Yue",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.524",
doi = "10.18653/v1/2020.acl-main.524",
pages = "5906--5917",
abstract = "Cross-domain NER is a challenging yet practical problem. Entity mentions can be highly different across domains. However, the correlations between entity types can be relatively more stable across domains. We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state. With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level. Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer. Empirically, experiments on four few-shot and zero-shot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jia-zhang-2020-multi">
<titleInfo>
<title>Multi-Cell Compositional LSTM for NER Domain Adaptation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chen</namePart>
<namePart type="family">Jia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yue</namePart>
<namePart type="family">Zhang</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 58th Annual Meeting of the Association for Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dan</namePart>
<namePart type="family">Jurafsky</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joyce</namePart>
<namePart type="family">Chai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natalie</namePart>
<namePart type="family">Schluter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joel</namePart>
<namePart type="family">Tetreault</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>Cross-domain NER is a challenging yet practical problem. Entity mentions can be highly different across domains. However, the correlations between entity types can be relatively more stable across domains. We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state. With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level. Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer. Empirically, experiments on four few-shot and zero-shot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.</abstract>
<identifier type="citekey">jia-zhang-2020-multi</identifier>
<identifier type="doi">10.18653/v1/2020.acl-main.524</identifier>
<location>
<url>https://aclanthology.org/2020.acl-main.524</url>
</location>
<part>
<date>2020-07</date>
<extent unit="page">
<start>5906</start>
<end>5917</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Multi-Cell Compositional LSTM for NER Domain Adaptation
%A Jia, Chen
%A Zhang, Yue
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F jia-zhang-2020-multi
%X Cross-domain NER is a challenging yet practical problem. Entity mentions can be highly different across domains. However, the correlations between entity types can be relatively more stable across domains. We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state. With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level. Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer. Empirically, experiments on four few-shot and zero-shot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.
%R 10.18653/v1/2020.acl-main.524
%U https://aclanthology.org/2020.acl-main.524
%U https://doi.org/10.18653/v1/2020.acl-main.524
%P 5906-5917
Markdown (Informal)
[Multi-Cell Compositional LSTM for NER Domain Adaptation](https://aclanthology.org/2020.acl-main.524) (Jia & Zhang, ACL 2020)
ACL