<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hu-etal-2022-dialogue">
    <titleInfo>
        <title>Dialogue Meaning Representation for Task-Oriented Dialogue Systems</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xiangkun</namePart>
        <namePart type="family">Hu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Junqi</namePart>
        <namePart type="family">Dai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hang</namePart>
        <namePart type="family">Yan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yi</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Qipeng</namePart>
        <namePart type="family">Guo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xipeng</namePart>
        <namePart type="family">Qiu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zheng</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2022</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, United Arab Emirates</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically follows the intent-slot framework, which is easy for annotation yet limited in scalability for complex linguistic expressions. A line of works alleviates the representation issue by introducing hierarchical structures but challenging to express complex compositional semantics, such as negation and coreference. We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue. Our representation contains a set of nodes and edges to represent rich compositional semantics. Moreover, we propose an inheritance hierarchy mechanism focusing on domain extensibility. Additionally, we annotated DMR-FastFood, a multi-turn dialogue dataset with more than 70k utterances, with DMR. We propose two evaluation tasks to evaluate different dialogue models and a novel coreference resolution model GNNCoref for the graph-based coreference resolution task. Experiments show that DMR can be parsed well with pre-trained Seq2Seq models, and GNNCoref outperforms the baseline models by a large margin. The dataset and code are available at https://github.com/amazon-research/dialogue-meaning-representation</abstract>
    <identifier type="citekey">hu-etal-2022-dialogue</identifier>
    <identifier type="doi">10.18653/v1/2022.findings-emnlp.17</identifier>
    <location>
        <url>https://aclanthology.org/2022.findings-emnlp.17</url>
    </location>
    <part>
        <date>2022-12</date>
        <extent unit="page">
            <start>223</start>
            <end>237</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Dialogue Meaning Representation for Task-Oriented Dialogue Systems
%A Hu, Xiangkun
%A Dai, Junqi
%A Yan, Hang
%A Zhang, Yi
%A Guo, Qipeng
%A Qiu, Xipeng
%A Zhang, Zheng
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F hu-etal-2022-dialogue
%X Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically follows the intent-slot framework, which is easy for annotation yet limited in scalability for complex linguistic expressions. A line of works alleviates the representation issue by introducing hierarchical structures but challenging to express complex compositional semantics, such as negation and coreference. We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue. Our representation contains a set of nodes and edges to represent rich compositional semantics. Moreover, we propose an inheritance hierarchy mechanism focusing on domain extensibility. Additionally, we annotated DMR-FastFood, a multi-turn dialogue dataset with more than 70k utterances, with DMR. We propose two evaluation tasks to evaluate different dialogue models and a novel coreference resolution model GNNCoref for the graph-based coreference resolution task. Experiments show that DMR can be parsed well with pre-trained Seq2Seq models, and GNNCoref outperforms the baseline models by a large margin. The dataset and code are available at https://github.com/amazon-research/dialogue-meaning-representation
%R 10.18653/v1/2022.findings-emnlp.17
%U https://aclanthology.org/2022.findings-emnlp.17
%U https://doi.org/10.18653/v1/2022.findings-emnlp.17
%P 223-237

Markdown (Informal)

[Dialogue Meaning Representation for Task-Oriented Dialogue Systems](https://aclanthology.org/2022.findings-emnlp.17) (Hu et al., Findings 2022)

• Dialogue Meaning Representation for Task-Oriented Dialogue Systems (Hu et al., Findings 2022)

ACL

• Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, and Zheng Zhang. 2022. Dialogue Meaning Representation for Task-Oriented Dialogue Systems. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 223–237, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.