<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhu-etal-2020-multitask">
    <titleInfo>
        <title>A Multitask Active Learning Framework for Natural Language Understanding</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hua</namePart>
        <namePart type="family">Zhu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wu</namePart>
        <namePart type="family">Ye</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sihan</namePart>
        <namePart type="family">Luo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xidong</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Natural language understanding (NLU) aims at identifying user intent and extracting semantic slots. This requires sufficient annotating data to get considerable performance in real-world situations. Active learning (AL) has been well-studied to decrease the needed amount of the annotating data and successfully applied to NLU. However, no research has been done on investigating how the relation information between intents and slots can improve the efficiency of AL algorithms. In this paper, we propose a multitask AL framework for NLU. Our framework enables pool-based AL algorithms to make use of the relation information between sub-tasks provided by a joint model, and we propose an efficient computation for the entropy of a joint model. Experimental results show our framework can achieve competitive performance with less training data than baseline methods on all datasets. We also demonstrate that when using the entropy as the query strategy, the model with complete relation information can perform better than those with partial information. Additionally, we demonstrate that the efficiency of these active learning algorithms in our framework is still effective when incorporate with the Bidirectional Encoder Representations from Transformers (BERT).</abstract>
    <identifier type="citekey">zhu-etal-2020-multitask</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.430</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-main.430</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>4900</start>
            <end>4914</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Multitask Active Learning Framework for Natural Language Understanding
%A Zhu, Hua
%A Ye, Wu
%A Luo, Sihan
%A Zhang, Xidong
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F zhu-etal-2020-multitask
%X Natural language understanding (NLU) aims at identifying user intent and extracting semantic slots. This requires sufficient annotating data to get considerable performance in real-world situations. Active learning (AL) has been well-studied to decrease the needed amount of the annotating data and successfully applied to NLU. However, no research has been done on investigating how the relation information between intents and slots can improve the efficiency of AL algorithms. In this paper, we propose a multitask AL framework for NLU. Our framework enables pool-based AL algorithms to make use of the relation information between sub-tasks provided by a joint model, and we propose an efficient computation for the entropy of a joint model. Experimental results show our framework can achieve competitive performance with less training data than baseline methods on all datasets. We also demonstrate that when using the entropy as the query strategy, the model with complete relation information can perform better than those with partial information. Additionally, we demonstrate that the efficiency of these active learning algorithms in our framework is still effective when incorporate with the Bidirectional Encoder Representations from Transformers (BERT).
%R 10.18653/v1/2020.coling-main.430
%U https://aclanthology.org/2020.coling-main.430
%U https://doi.org/10.18653/v1/2020.coling-main.430
%P 4900-4914

Markdown (Informal)

[A Multitask Active Learning Framework for Natural Language Understanding](https://aclanthology.org/2020.coling-main.430) (Zhu et al., COLING 2020)

• A Multitask Active Learning Framework for Natural Language Understanding (Zhu et al., COLING 2020)

ACL

• Hua Zhu, Wu Ye, Sihan Luo, and Xidong Zhang. 2020. A Multitask Active Learning Framework for Natural Language Understanding. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4900–4914, Barcelona, Spain (Online). International Committee on Computational Linguistics.