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        <title>Unsupervised Learning of KB Queries in Task-Oriented Dialogs</title>
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    <name type="personal">
        <namePart type="given">Dinesh</namePart>
        <namePart type="family">Raghu</namePart>
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    <name type="personal">
        <namePart type="given">Nikhil</namePart>
        <namePart type="family">Gupta</namePart>
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        <namePart>Mausam</namePart>
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    <abstract>Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries—these annotations can be time consuming, and expensive. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in subsequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but important modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation.</abstract>
    <identifier type="citekey">raghu-etal-2021-unsupervised</identifier>
    <identifier type="doi">10.1162/tacl_a_00372</identifier>
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    <part>
        <date>2021</date>
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            <start>374</start>
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%0 Journal Article
%T Unsupervised Learning of KB Queries in Task-Oriented Dialogs
%A Raghu, Dinesh
%A Gupta, Nikhil
%A Mausam
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F raghu-etal-2021-unsupervised
%X Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries—these annotations can be time consuming, and expensive. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in subsequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but important modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation.
%R 10.1162/tacl_a_00372
%U https://aclanthology.org/2021.tacl-1.23
%U https://doi.org/10.1162/tacl_a_00372
%P 374-390

Markdown (Informal)

[Unsupervised Learning of KB Queries in Task-Oriented Dialogs](https://aclanthology.org/2021.tacl-1.23) (Raghu et al., TACL 2021)

• Unsupervised Learning of KB Queries in Task-Oriented Dialogs (Raghu et al., TACL 2021)

ACL

• Dinesh Raghu, Nikhil Gupta, and Mausam. 2021. Unsupervised Learning of KB Queries in Task-Oriented Dialogs. Transactions of the Association for Computational Linguistics, 9:374–390.