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        <title>Grounding learning of modifier dynamics: An application to color naming</title>
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    <name type="personal">
        <namePart type="given">Xudong</namePart>
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        <namePart type="given">Philip</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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            <namePart type="given">Jing</namePart>
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            <namePart type="given">Xiaojun</namePart>
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    <abstract>Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as “light blue”. We present a model of color modifiers that, compared with previous additive models in RGB space, learns more complex transformations. In addition, we present a model that operates in the HSV color space. We show that certain adjectives are better modeled in that space. To account for all modifiers, we train a hard ensemble model that selects a color space depending on the modifier-color pair. Experimental results show significant and consistent improvements compared to the state-of-the-art baseline model.</abstract>
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%0 Conference Proceedings
%T Grounding learning of modifier dynamics: An application to color naming
%A Han, Xudong
%A Schulz, Philip
%A Cohn, Trevor
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F han-etal-2019-grounding
%X Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as “light blue”. We present a model of color modifiers that, compared with previous additive models in RGB space, learns more complex transformations. In addition, we present a model that operates in the HSV color space. We show that certain adjectives are better modeled in that space. To account for all modifiers, we train a hard ensemble model that selects a color space depending on the modifier-color pair. Experimental results show significant and consistent improvements compared to the state-of-the-art baseline model.
%R 10.18653/v1/D19-1158
%U https://aclanthology.org/D19-1158
%U https://doi.org/10.18653/v1/D19-1158
%P 1488-1493

Markdown (Informal)

[Grounding learning of modifier dynamics: An application to color naming](https://aclanthology.org/D19-1158) (Han et al., EMNLP-IJCNLP 2019)

• Grounding learning of modifier dynamics: An application to color naming (Han et al., EMNLP-IJCNLP 2019)

ACL

• Xudong Han, Philip Schulz, and Trevor Cohn. 2019. Grounding learning of modifier dynamics: An application to color naming. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1488–1493, Hong Kong, China. Association for Computational Linguistics.