<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="andersen-etal-2020-word">
    <titleInfo>
        <title>Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jakob</namePart>
        <namePart type="given">Smedegaard</namePart>
        <namePart type="family">Andersen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tom</namePart>
        <namePart type="family">Schöner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Walid</namePart>
        <namePart type="family">Maalej</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>Estimating uncertainties of Neural Network predictions paves the way towards more reliable and trustful text classifications. However, common uncertainty estimation approaches remain as black-boxes without explaining which features have led to the uncertainty of a prediction. This hinders users from understanding the cause of unreliable model behaviour. We introduce an approach to decompose and visualize the uncertainty of text classifiers at the level of words. Our approach builds on top of Recurrent Neural Networks and Bayesian modelling in order to provide detailed explanations of uncertainties, enabling a deeper reasoning about unreliable model behaviours. We conduct a preliminary experiment to check the impact and correctness of our approach. By explaining and investigating the predictive uncertainties of a sentiment analysis task, we argue that our approach is able to provide a more profound understanding of artificial decision making.</abstract>
    <identifier type="citekey">andersen-etal-2020-word</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.484</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-main.484</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>5541</start>
            <end>5546</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs
%A Andersen, Jakob Smedegaard
%A Schöner, Tom
%A Maalej, Walid
%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 andersen-etal-2020-word
%X Estimating uncertainties of Neural Network predictions paves the way towards more reliable and trustful text classifications. However, common uncertainty estimation approaches remain as black-boxes without explaining which features have led to the uncertainty of a prediction. This hinders users from understanding the cause of unreliable model behaviour. We introduce an approach to decompose and visualize the uncertainty of text classifiers at the level of words. Our approach builds on top of Recurrent Neural Networks and Bayesian modelling in order to provide detailed explanations of uncertainties, enabling a deeper reasoning about unreliable model behaviours. We conduct a preliminary experiment to check the impact and correctness of our approach. By explaining and investigating the predictive uncertainties of a sentiment analysis task, we argue that our approach is able to provide a more profound understanding of artificial decision making.
%R 10.18653/v1/2020.coling-main.484
%U https://aclanthology.org/2020.coling-main.484
%U https://doi.org/10.18653/v1/2020.coling-main.484
%P 5541-5546

Markdown (Informal)

[Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs](https://aclanthology.org/2020.coling-main.484) (Andersen et al., COLING 2020)

• Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs (Andersen et al., COLING 2020)

ACL

• Jakob Smedegaard Andersen, Tom Schöner, and Walid Maalej. 2020. Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5541–5546, Barcelona, Spain (Online). International Committee on Computational Linguistics.