<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shi-etal-2023-autoreply">
    <titleInfo>
        <title>AutoReply: Detecting Nonsense in Dialogue with Discriminative Replies</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Weiyan</namePart>
        <namePart type="family">Shi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Emily</namePart>
        <namePart type="family">Dinan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Adi</namePart>
        <namePart type="family">Renduchintala</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Fried</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Athul</namePart>
        <namePart type="family">Jacob</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhou</namePart>
        <namePart type="family">Yu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mike</namePart>
        <namePart type="family">Lewis</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We show that dialogue models can detect errors in their own messages, by calculating the likelihood of replies that are indicative of poor messages. For example, if an agent believes its partner is likely to respond “I don’t understand” to a candidate message, that message may not make sense, so an alternative message should be chosen. We evaluate our approach on a dataset from the game Diplomacy, which contains long dialogues richly grounded in the game state, on which existing models make many errors. We first show that hand-crafted replies can be effective for the task of detecting nonsense in applications as complex as Diplomacy. We then design AutoReply, an algorithm to search for such discriminative replies automatically, given a small number of annotated dialogue examples. We find that AutoReply-generated replies outperform handcrafted replies and perform on par with supervised learning approaches.</abstract>
    <identifier type="citekey">shi-etal-2023-autoreply</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.23</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.23</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>294</start>
            <end>309</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T AutoReply: Detecting Nonsense in Dialogue with Discriminative Replies
%A Shi, Weiyan
%A Dinan, Emily
%A Renduchintala, Adi
%A Fried, Daniel
%A Jacob, Athul
%A Yu, Zhou
%A Lewis, Mike
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F shi-etal-2023-autoreply
%X We show that dialogue models can detect errors in their own messages, by calculating the likelihood of replies that are indicative of poor messages. For example, if an agent believes its partner is likely to respond “I don’t understand” to a candidate message, that message may not make sense, so an alternative message should be chosen. We evaluate our approach on a dataset from the game Diplomacy, which contains long dialogues richly grounded in the game state, on which existing models make many errors. We first show that hand-crafted replies can be effective for the task of detecting nonsense in applications as complex as Diplomacy. We then design AutoReply, an algorithm to search for such discriminative replies automatically, given a small number of annotated dialogue examples. We find that AutoReply-generated replies outperform handcrafted replies and perform on par with supervised learning approaches.
%R 10.18653/v1/2023.findings-emnlp.23
%U https://aclanthology.org/2023.findings-emnlp.23
%U https://doi.org/10.18653/v1/2023.findings-emnlp.23
%P 294-309

Markdown (Informal)

[AutoReply: Detecting Nonsense in Dialogue with Discriminative Replies](https://aclanthology.org/2023.findings-emnlp.23) (Shi et al., Findings 2023)

• AutoReply: Detecting Nonsense in Dialogue with Discriminative Replies (Shi et al., Findings 2023)

ACL

• Weiyan Shi, Emily Dinan, Adi Renduchintala, Daniel Fried, Athul Jacob, Zhou Yu, and Mike Lewis. 2023. AutoReply: Detecting Nonsense in Dialogue with Discriminative Replies. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 294–309, Singapore. Association for Computational Linguistics.