<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2023-dialog">
    <titleInfo>
        <title>Dialog Action-Aware Transformer for Dialog Policy Learning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Huimin</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wai</namePart>
        <namePart type="given">Chung</namePart>
        <namePart type="family">Kwan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kam-Fai</namePart>
        <namePart type="family">Wong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Svetlana</namePart>
            <namePart type="family">Stoyanchev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shafiq</namePart>
            <namePart type="family">Joty</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Schlangen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ondrej</namePart>
            <namePart type="family">Dusek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Casey</namePart>
            <namePart type="family">Kennington</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Malihe</namePart>
            <namePart type="family">Alikhani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Prague, Czechia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Recent works usually address Dialog policy learning DPL by training a reinforcement learning (RL) agent to determine the best dialog action. However, existing works on deep RL require a large volume of agent-user interactions to achieve acceptable performance. In this paper, we propose to make full use of the plain text knowledge from the pre-trained language model to accelerate the RL agent’s learning speed. Specifically, we design a dialog action-aware transformer encoder (DaTrans), which integrates a new fine-tuning procedure named masked last action task to encourage DaTrans to be dialog-aware and distill action-specific features. Then, DaTrans is further optimized in an RL setting with ongoing interactions and evolves through exploration in the dialog action space toward maximizing long-term accumulated rewards. The effectiveness and efficiency of the proposed model are demonstrated with both simulator evaluation and human evaluation.</abstract>
    <identifier type="citekey">wang-etal-2023-dialog</identifier>
    <identifier type="doi">10.18653/v1/2023.sigdial-1.12</identifier>
    <location>
        <url>https://aclanthology.org/2023.sigdial-1.12</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>142</start>
            <end>148</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Dialog Action-Aware Transformer for Dialog Policy Learning
%A Wang, Huimin
%A Kwan, Wai Chung
%A Wong, Kam-Fai
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F wang-etal-2023-dialog
%X Recent works usually address Dialog policy learning DPL by training a reinforcement learning (RL) agent to determine the best dialog action. However, existing works on deep RL require a large volume of agent-user interactions to achieve acceptable performance. In this paper, we propose to make full use of the plain text knowledge from the pre-trained language model to accelerate the RL agent’s learning speed. Specifically, we design a dialog action-aware transformer encoder (DaTrans), which integrates a new fine-tuning procedure named masked last action task to encourage DaTrans to be dialog-aware and distill action-specific features. Then, DaTrans is further optimized in an RL setting with ongoing interactions and evolves through exploration in the dialog action space toward maximizing long-term accumulated rewards. The effectiveness and efficiency of the proposed model are demonstrated with both simulator evaluation and human evaluation.
%R 10.18653/v1/2023.sigdial-1.12
%U https://aclanthology.org/2023.sigdial-1.12
%U https://doi.org/10.18653/v1/2023.sigdial-1.12
%P 142-148

Markdown (Informal)

[Dialog Action-Aware Transformer for Dialog Policy Learning](https://aclanthology.org/2023.sigdial-1.12) (Wang et al., SIGDIAL 2023)

• Dialog Action-Aware Transformer for Dialog Policy Learning (Wang et al., SIGDIAL 2023)

ACL

• Huimin Wang, Wai Chung Kwan, and Kam-Fai Wong. 2023. Dialog Action-Aware Transformer for Dialog Policy Learning. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 142–148, Prague, Czechia. Association for Computational Linguistics.