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        <title>TIGS: An Inference Algorithm for Text Infilling with Gradient Search</title>
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        <namePart type="given">Dayiheng</namePart>
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    <name type="personal">
        <namePart type="given">Jiancheng</namePart>
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    <abstract>Text infilling aims at filling in the missing part of a sentence or paragraph, which has been applied to a variety of real-world natural language generation scenarios. Given a well-trained sequential generative model, it is challenging for its unidirectional decoder to generate missing symbols conditioned on the past and future information around the missing part. In this paper, we propose an iterative inference algorithm based on gradient search, which could be the first inference algorithm that can be broadly applied to any neural sequence generative models for text infilling tasks. Extensive experimental comparisons show the effectiveness and efficiency of the proposed method on three different text infilling tasks with various mask ratios and different mask strategies, comparing with five state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T TIGS: An Inference Algorithm for Text Infilling with Gradient Search
%A Liu, Dayiheng
%A Fu, Jie
%A Liu, Pengfei
%A Lv, Jiancheng
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F liu-etal-2019-tigs
%X Text infilling aims at filling in the missing part of a sentence or paragraph, which has been applied to a variety of real-world natural language generation scenarios. Given a well-trained sequential generative model, it is challenging for its unidirectional decoder to generate missing symbols conditioned on the past and future information around the missing part. In this paper, we propose an iterative inference algorithm based on gradient search, which could be the first inference algorithm that can be broadly applied to any neural sequence generative models for text infilling tasks. Extensive experimental comparisons show the effectiveness and efficiency of the proposed method on three different text infilling tasks with various mask ratios and different mask strategies, comparing with five state-of-the-art methods.
%R 10.18653/v1/P19-1406
%U https://aclanthology.org/P19-1406
%U https://doi.org/10.18653/v1/P19-1406
%P 4146-4156

Markdown (Informal)

[TIGS: An Inference Algorithm for Text Infilling with Gradient Search](https://aclanthology.org/P19-1406) (Liu et al., ACL 2019)

• TIGS: An Inference Algorithm for Text Infilling with Gradient Search (Liu et al., ACL 2019)

ACL

• Dayiheng Liu, Jie Fu, Pengfei Liu, and Jiancheng Lv. 2019. TIGS: An Inference Algorithm for Text Infilling with Gradient Search. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4146–4156, Florence, Italy. Association for Computational Linguistics.