<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="oguz-vu-2021-shot">
    <titleInfo>
        <title>Few-shot Learning for Slot Tagging with Attentive Relational Network</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Cennet</namePart>
        <namePart type="family">Oguz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ngoc</namePart>
        <namePart type="given">Thang</namePart>
        <namePart type="family">Vu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Paola</namePart>
            <namePart type="family">Merlo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jorg</namePart>
            <namePart type="family">Tiedemann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Reut</namePart>
            <namePart type="family">Tsarfaty</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>Metric-based learning is a well-known family of methods for few-shot learning, especially in computer vision. Recently, they have been used in many natural language processing applications but not for slot tagging. In this paper, we explore metric-based learning methods in the slot tagging task and propose a novel metric-based learning architecture - Attentive Relational Network. Our proposed method extends relation networks, making them more suitable for natural language processing applications in general, by leveraging pretrained contextual embeddings such as ELMO and BERT and by using attention mechanism. The results on SNIPS data show that our proposed method outperforms other state of the art metric-based learning methods.</abstract>
    <identifier type="citekey">oguz-vu-2021-shot</identifier>
    <identifier type="doi">10.18653/v1/2021.eacl-main.134</identifier>
    <location>
        <url>https://aclanthology.org/2021.eacl-main.134</url>
    </location>
    <part>
        <date>2021-04</date>
        <extent unit="page">
            <start>1566</start>
            <end>1572</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Few-shot Learning for Slot Tagging with Attentive Relational Network
%A Oguz, Cennet
%A Vu, Ngoc Thang
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F oguz-vu-2021-shot
%X Metric-based learning is a well-known family of methods for few-shot learning, especially in computer vision. Recently, they have been used in many natural language processing applications but not for slot tagging. In this paper, we explore metric-based learning methods in the slot tagging task and propose a novel metric-based learning architecture - Attentive Relational Network. Our proposed method extends relation networks, making them more suitable for natural language processing applications in general, by leveraging pretrained contextual embeddings such as ELMO and BERT and by using attention mechanism. The results on SNIPS data show that our proposed method outperforms other state of the art metric-based learning methods.
%R 10.18653/v1/2021.eacl-main.134
%U https://aclanthology.org/2021.eacl-main.134
%U https://doi.org/10.18653/v1/2021.eacl-main.134
%P 1566-1572

Markdown (Informal)

[Few-shot Learning for Slot Tagging with Attentive Relational Network](https://aclanthology.org/2021.eacl-main.134) (Oguz & Vu, EACL 2021)

• Few-shot Learning for Slot Tagging with Attentive Relational Network (Oguz & Vu, EACL 2021)

ACL

• Cennet Oguz and Ngoc Thang Vu. 2021. Few-shot Learning for Slot Tagging with Attentive Relational Network. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1566–1572, Online. Association for Computational Linguistics.