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    <titleInfo>
        <title>Counterfactual Language Model Adaptation for Suggesting Phrases</title>
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    <name type="personal">
        <namePart type="given">Kenneth</namePart>
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    <name type="personal">
        <namePart type="given">Kai-Wei</namePart>
        <namePart type="family">Chang</namePart>
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        <namePart type="given">Adam</namePart>
        <namePart type="family">Kalai</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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        <name type="personal">
            <namePart type="given">Taro</namePart>
            <namePart type="family">Watanabe</namePart>
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    <abstract>Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions that writers accept, a related but distinct task to making accurate predictions. Although this task is fundamentally interactive, we propose a counterfactual setting that permits offline training and evaluation. We find that even a simple language model can capture text characteristics that improve acceptability.</abstract>
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        <url>https://aclanthology.org/I17-2009</url>
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%0 Conference Proceedings
%T Counterfactual Language Model Adaptation for Suggesting Phrases
%A Arnold, Kenneth
%A Chang, Kai-Wei
%A Kalai, Adam
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F arnold-etal-2017-counterfactual
%X Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions that writers accept, a related but distinct task to making accurate predictions. Although this task is fundamentally interactive, we propose a counterfactual setting that permits offline training and evaluation. We find that even a simple language model can capture text characteristics that improve acceptability.
%U https://aclanthology.org/I17-2009
%P 49-54

Markdown (Informal)

[Counterfactual Language Model Adaptation for Suggesting Phrases](https://aclanthology.org/I17-2009) (Arnold et al., IJCNLP 2017)

• Counterfactual Language Model Adaptation for Suggesting Phrases (Arnold et al., IJCNLP 2017)

ACL

• Kenneth Arnold, Kai-Wei Chang, and Adam Kalai. 2017. Counterfactual Language Model Adaptation for Suggesting Phrases. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 49–54, Taipei, Taiwan. Asian Federation of Natural Language Processing.