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        <title>Template Guided Text Generation for Task-Oriented Dialogue</title>
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    <name type="personal">
        <namePart type="given">Mihir</namePart>
        <namePart type="family">Kale</namePart>
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        <namePart type="given">Abhinav</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Virtual assistants such as Google Assistant, Amazon Alexa, and Apple Siri enable users to interact with a large number of services and APIs on the web using natural language. In this work, we investigate two methods for Natural Language Generation (NLG) using a single domain-independent model across a large number of APIs. First, we propose a schema-guided approach which conditions the generation on a schema describing the API in natural language. Our second method investigates the use of a small number of templates, growing linearly in number of slots, to convey the semantics of the API. To generate utterances for an arbitrary slot combination, a few simple templates are first concatenated to give a semantically correct, but possibly incoherent and ungrammatical utterance. A pre-trained language model is subsequently employed to rewrite it into coherent, natural sounding text. Through automatic metrics and human evaluation, we show that our method improves over strong baselines, is robust to out-of-domain inputs and shows improved sample efficiency.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.527</identifier>
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%0 Conference Proceedings
%T Template Guided Text Generation for Task-Oriented Dialogue
%A Kale, Mihir
%A Rastogi, Abhinav
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kale-rastogi-2020-template
%X Virtual assistants such as Google Assistant, Amazon Alexa, and Apple Siri enable users to interact with a large number of services and APIs on the web using natural language. In this work, we investigate two methods for Natural Language Generation (NLG) using a single domain-independent model across a large number of APIs. First, we propose a schema-guided approach which conditions the generation on a schema describing the API in natural language. Our second method investigates the use of a small number of templates, growing linearly in number of slots, to convey the semantics of the API. To generate utterances for an arbitrary slot combination, a few simple templates are first concatenated to give a semantically correct, but possibly incoherent and ungrammatical utterance. A pre-trained language model is subsequently employed to rewrite it into coherent, natural sounding text. Through automatic metrics and human evaluation, we show that our method improves over strong baselines, is robust to out-of-domain inputs and shows improved sample efficiency.
%R 10.18653/v1/2020.emnlp-main.527
%U https://aclanthology.org/2020.emnlp-main.527
%U https://doi.org/10.18653/v1/2020.emnlp-main.527
%P 6505-6520

Markdown (Informal)

[Template Guided Text Generation for Task-Oriented Dialogue](https://aclanthology.org/2020.emnlp-main.527) (Kale & Rastogi, EMNLP 2020)

• Template Guided Text Generation for Task-Oriented Dialogue (Kale & Rastogi, EMNLP 2020)

ACL

• Mihir Kale and Abhinav Rastogi. 2020. Template Guided Text Generation for Task-Oriented Dialogue. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6505–6520, Online. Association for Computational Linguistics.