<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-zhang-2021-semi">
    <titleInfo>
        <title>Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yue</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jiong</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mitra</namePart>
            <namePart type="family">Mohtarami</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shang-Wen</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Di</namePart>
            <namePart type="family">Jin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mandy</namePart>
            <namePart type="family">Korpusik</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shuyan</namePart>
            <namePart type="family">Dong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</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">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dilek</namePart>
            <namePart type="family">Hakkani-Tur</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>Meta learning aims to optimize the model’s capability to generalize to new tasks and domains. Lacking a data-efficient way to create meta training tasks has prevented the application of meta-learning to the real-world few shot learning scenarios. Recent studies have proposed unsupervised approaches to create meta-training tasks from unlabeled data for free, e.g., the SMLMT method (Bansal et al., 2020a) constructs unsupervised multi-class classification tasks from the unlabeled text by randomly masking words in the sentence and let the meta learner choose which word to fill in the blank. This study proposes a semi-supervised meta-learning approach that incorporates both the representation power of large pre-trained language models and the generalization capability of prototypical networks enhanced by SMLMT. The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples. By incorporating SMLMT with prototypical networks, the meta learner generalizes better to unseen domains and gains higher accuracy on out-of-scope examples without the heavy lifting of pre-training. We observe significant improvement in few-shot generalization after training only a few epochs on the intent classification tasks evaluated in a multi-domain setting.</abstract>
    <identifier type="citekey">li-zhang-2021-semi</identifier>
    <identifier type="doi">10.18653/v1/2021.metanlp-1.8</identifier>
    <location>
        <url>https://aclanthology.org/2021.metanlp-1.8</url>
    </location>
    <part>
        <date>2021-08</date>
        <extent unit="page">
            <start>67</start>
            <end>75</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification
%A Li, Yue
%A Zhang, Jiong
%Y Lee, Hung-Yi
%Y Mohtarami, Mitra
%Y Li, Shang-Wen
%Y Jin, Di
%Y Korpusik, Mandy
%Y Dong, Shuyan
%Y Vu, Ngoc Thang
%Y Hakkani-Tur, Dilek
%S Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F li-zhang-2021-semi
%X Meta learning aims to optimize the model’s capability to generalize to new tasks and domains. Lacking a data-efficient way to create meta training tasks has prevented the application of meta-learning to the real-world few shot learning scenarios. Recent studies have proposed unsupervised approaches to create meta-training tasks from unlabeled data for free, e.g., the SMLMT method (Bansal et al., 2020a) constructs unsupervised multi-class classification tasks from the unlabeled text by randomly masking words in the sentence and let the meta learner choose which word to fill in the blank. This study proposes a semi-supervised meta-learning approach that incorporates both the representation power of large pre-trained language models and the generalization capability of prototypical networks enhanced by SMLMT. The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples. By incorporating SMLMT with prototypical networks, the meta learner generalizes better to unseen domains and gains higher accuracy on out-of-scope examples without the heavy lifting of pre-training. We observe significant improvement in few-shot generalization after training only a few epochs on the intent classification tasks evaluated in a multi-domain setting.
%R 10.18653/v1/2021.metanlp-1.8
%U https://aclanthology.org/2021.metanlp-1.8
%U https://doi.org/10.18653/v1/2021.metanlp-1.8
%P 67-75

Markdown (Informal)

[Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification](https://aclanthology.org/2021.metanlp-1.8) (Li & Zhang, MetaNLP 2021)

• Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification (Li & Zhang, MetaNLP 2021)

ACL

• Yue Li and Jiong Zhang. 2021. Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification. In Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing, pages 67–75, Online. Association for Computational Linguistics.