@inproceedings{baofeng-etal-2024-udaa,
title = "{UDAA}: An Unsupervised Domain Adaptation Adversarial Learning Framework for Zero-Resource Cross-Domain Named Entity Recognition",
author = "Li, Baofeng and
Tang, Jianguo and
Qin, Yu and
Xu, Yuelou and
Lu, Yan and
Wang, Kai and
Li, Lei and
Zhou, Yanquan",
editor = "Maosong, Sun and
Jiye, Liang and
Xianpei, Han and
Zhiyuan, Liu and
Yulan, He",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.87/",
pages = "1123--1135",
language = "eng",
abstract = "``The zero-resource cross-domain named entity recognition (NER) task aims to perform NER in aspecific domain where labeled data is unavailable. Existing methods primarily focus on transfer-ring NER knowledge from high-resource to zero-resource domains. However, the challenge liesin effectively transferring NER knowledge between domains due to the inherent differences inentity structures across domains. To tackle this challenge, we propose an Unsupervised DomainAdaptation Adversarial (UDAA) framework, which combines the masked language model auxil-iary task with the domain adaptive adversarial network to mitigate inter-domain differences andefficiently facilitate knowledge transfer. Experimental results on CBS, Twitter, and WNUT2016three datasets demonstrate the effectiveness of our framework. Notably, we achieved new state-of-the-art performance on the three datasets. Our code will be released.Introduction''"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="baofeng-etal-2024-udaa">
<titleInfo>
<title>UDAA: An Unsupervised Domain Adaptation Adversarial Learning Framework for Zero-Resource Cross-Domain Named Entity Recognition</title>
</titleInfo>
<name type="personal">
<namePart type="given">Baofeng</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jianguo</namePart>
<namePart type="family">Tang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu</namePart>
<namePart type="family">Qin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuelou</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yan</namePart>
<namePart type="family">Lu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lei</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yanquan</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">eng</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sun</namePart>
<namePart type="family">Maosong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liang</namePart>
<namePart type="family">Jiye</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Han</namePart>
<namePart type="family">Xianpei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liu</namePart>
<namePart type="family">Zhiyuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">He</namePart>
<namePart type="family">Yulan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“The zero-resource cross-domain named entity recognition (NER) task aims to perform NER in aspecific domain where labeled data is unavailable. Existing methods primarily focus on transfer-ring NER knowledge from high-resource to zero-resource domains. However, the challenge liesin effectively transferring NER knowledge between domains due to the inherent differences inentity structures across domains. To tackle this challenge, we propose an Unsupervised DomainAdaptation Adversarial (UDAA) framework, which combines the masked language model auxil-iary task with the domain adaptive adversarial network to mitigate inter-domain differences andefficiently facilitate knowledge transfer. Experimental results on CBS, Twitter, and WNUT2016three datasets demonstrate the effectiveness of our framework. Notably, we achieved new state-of-the-art performance on the three datasets. Our code will be released.Introduction”</abstract>
<identifier type="citekey">baofeng-etal-2024-udaa</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.87/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>1123</start>
<end>1135</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UDAA: An Unsupervised Domain Adaptation Adversarial Learning Framework for Zero-Resource Cross-Domain Named Entity Recognition
%A Li, Baofeng
%A Tang, Jianguo
%A Qin, Yu
%A Xu, Yuelou
%A Lu, Yan
%A Wang, Kai
%A Li, Lei
%A Zhou, Yanquan
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F baofeng-etal-2024-udaa
%X “The zero-resource cross-domain named entity recognition (NER) task aims to perform NER in aspecific domain where labeled data is unavailable. Existing methods primarily focus on transfer-ring NER knowledge from high-resource to zero-resource domains. However, the challenge liesin effectively transferring NER knowledge between domains due to the inherent differences inentity structures across domains. To tackle this challenge, we propose an Unsupervised DomainAdaptation Adversarial (UDAA) framework, which combines the masked language model auxil-iary task with the domain adaptive adversarial network to mitigate inter-domain differences andefficiently facilitate knowledge transfer. Experimental results on CBS, Twitter, and WNUT2016three datasets demonstrate the effectiveness of our framework. Notably, we achieved new state-of-the-art performance on the three datasets. Our code will be released.Introduction”
%U https://aclanthology.org/2024.ccl-1.87/
%P 1123-1135
Markdown (Informal)
[UDAA: An Unsupervised Domain Adaptation Adversarial Learning Framework for Zero-Resource Cross-Domain Named Entity Recognition](https://aclanthology.org/2024.ccl-1.87/) (Li et al., CCL 2024)
ACL
- Baofeng Li, Jianguo Tang, Yu Qin, Yuelou Xu, Yan Lu, Kai Wang, Lei Li, and Yanquan Zhou. 2024. UDAA: An Unsupervised Domain Adaptation Adversarial Learning Framework for Zero-Resource Cross-Domain Named Entity Recognition. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1123–1135, Taiyuan, China. Chinese Information Processing Society of China.