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        <title>Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching</title>
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    <name type="personal">
        <namePart type="given">Baolin</namePart>
        <namePart type="family">Peng</namePart>
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    <name type="personal">
        <namePart type="given">Chunyuan</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Shahin</namePart>
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    <name type="personal">
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    <abstract>We present a new method, Soloist,1 that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i)Soloist creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, Soloist significantly outperforms existing methods; and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00399</identifier>
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%0 Journal Article
%T Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching
%A Peng, Baolin
%A Li, Chunyuan
%A Li, Jinchao
%A Shayandeh, Shahin
%A Liden, Lars
%A Gao, Jianfeng
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F peng-etal-2021-soloist
%X We present a new method, Soloist,1 that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i)Soloist creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, Soloist significantly outperforms existing methods; and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.
%R 10.1162/tacl_a_00399
%U https://aclanthology.org/2021.tacl-1.49
%U https://doi.org/10.1162/tacl_a_00399
%P 807-824

Markdown (Informal)

[Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://aclanthology.org/2021.tacl-1.49) (Peng et al., TACL 2021)

• Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching (Peng et al., TACL 2021)

ACL

• Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, and Jianfeng Gao. 2021. Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching. Transactions of the Association for Computational Linguistics, 9:807–824.