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        <title>A case study on context-bound referring expression generation</title>
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        <namePart type="given">Maurice</namePart>
        <namePart type="family">Langner</namePart>
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        <dateIssued>2019-oct–nov</dateIssued>
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            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
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        <name type="personal">
            <namePart type="given">Kees</namePart>
            <namePart type="family">van Deemter</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Chenghua</namePart>
            <namePart type="family">Lin</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Hiroya</namePart>
            <namePart type="family">Takamura</namePart>
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    <abstract>In recent years, Bayesian models of referring expression generation have gained prominence in order to produce situationally more adequate referring expressions. Basically, these models enable the integration of different parameters into the decision process for using a specific referring expression like the cardinality of the object set, the configuration and complexity of the visual field, and the discriminatory power of available attributes that need to be combined with visual salience and personal preference. This paper describes and discusses the results of an empirical study on the production of referring expressions in visual fields with different object configurations of varying complexity and different contextual premises for using a referring expression. The visual fields are set up using data from the TUNA experiment with plain random or pragmatically enriched configurations which allow for target inference. Different categories of the situational contexts, in which the referring expressions are produced, provide different degrees of cooperativeness, so that generation quality and its relations to contextual user intention can be observed. The results of the study suggest that Bayesian approaches must integrate individual generation preference and the cooperativeness of the situational task in order to model the broad variance between speakers more adequately.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8603</identifier>
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        <date>2019-oct–nov</date>
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%0 Conference Proceedings
%T A case study on context-bound referring expression generation
%A Langner, Maurice
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F langner-2019-case
%X In recent years, Bayesian models of referring expression generation have gained prominence in order to produce situationally more adequate referring expressions. Basically, these models enable the integration of different parameters into the decision process for using a specific referring expression like the cardinality of the object set, the configuration and complexity of the visual field, and the discriminatory power of available attributes that need to be combined with visual salience and personal preference. This paper describes and discusses the results of an empirical study on the production of referring expressions in visual fields with different object configurations of varying complexity and different contextual premises for using a referring expression. The visual fields are set up using data from the TUNA experiment with plain random or pragmatically enriched configurations which allow for target inference. Different categories of the situational contexts, in which the referring expressions are produced, provide different degrees of cooperativeness, so that generation quality and its relations to contextual user intention can be observed. The results of the study suggest that Bayesian approaches must integrate individual generation preference and the cooperativeness of the situational task in order to model the broad variance between speakers more adequately.
%R 10.18653/v1/W19-8603
%U https://aclanthology.org/W19-8603
%U https://doi.org/10.18653/v1/W19-8603
%P 19-23

Markdown (Informal)

[A case study on context-bound referring expression generation](https://aclanthology.org/W19-8603) (Langner, INLG 2019)

• A case study on context-bound referring expression generation (Langner, INLG 2019)

ACL

• Maurice Langner. 2019. A case study on context-bound referring expression generation. In Proceedings of the 12th International Conference on Natural Language Generation, pages 19–23, Tokyo, Japan. Association for Computational Linguistics.