<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ung-etal-2022-saferdialogues">
    <titleInfo>
        <title>SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Megan</namePart>
        <namePart type="family">Ung</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jing</namePart>
        <namePart type="family">Xu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Y-Lan</namePart>
        <namePart type="family">Boureau</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Current open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures. However, current state-of-the-art models tend to react to feedback with defensive or oblivious responses. This makes for an unpleasant experience and may discourage conversation partners from giving feedback in the future. This work proposes SaFeRDialogues, a task and dataset of graceful responses to conversational feedback about safety failures. We collect a dataset of 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback. We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability.</abstract>
    <identifier type="citekey">ung-etal-2022-saferdialogues</identifier>
    <identifier type="doi">10.18653/v1/2022.acl-long.447</identifier>
    <location>
        <url>https://aclanthology.org/2022.acl-long.447</url>
    </location>
    <part>
        <date>2022-05</date>
        <extent unit="page">
            <start>6462</start>
            <end>6481</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures
%A Ung, Megan
%A Xu, Jing
%A Boureau, Y-Lan
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ung-etal-2022-saferdialogues
%X Current open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures. However, current state-of-the-art models tend to react to feedback with defensive or oblivious responses. This makes for an unpleasant experience and may discourage conversation partners from giving feedback in the future. This work proposes SaFeRDialogues, a task and dataset of graceful responses to conversational feedback about safety failures. We collect a dataset of 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback. We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability.
%R 10.18653/v1/2022.acl-long.447
%U https://aclanthology.org/2022.acl-long.447
%U https://doi.org/10.18653/v1/2022.acl-long.447
%P 6462-6481

Markdown (Informal)

[SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures](https://aclanthology.org/2022.acl-long.447) (Ung et al., ACL 2022)

• SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures (Ung et al., ACL 2022)

ACL

• Megan Ung, Jing Xu, and Y-Lan Boureau. 2022. SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6462–6481, Dublin, Ireland. Association for Computational Linguistics.