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        <title>Obtaining referential word meanings from visual and distributional information: Experiments on object naming</title>
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    <abstract>We investigate object naming, which is an important sub-task of referring expression generation on real-world images. As opposed to mutually exclusive labels used in object recognition, object names are more flexible, subject to communicative preferences and semantically related to each other. Therefore, we investigate models of referential word meaning that link visual to lexical information which we assume to be given through distributional word embeddings. We present a model that learns individual predictors for object names that link visual and distributional aspects of word meaning during training. We show that this is particularly beneficial for zero-shot learning, as compared to projecting visual objects directly into the distributional space. In a standard object naming task, we find that different ways of combining lexical and visual information achieve very similar performance, though experiments on model combination suggest that they capture complementary aspects of referential meaning.</abstract>
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%0 Conference Proceedings
%T Obtaining referential word meanings from visual and distributional information: Experiments on object naming
%A Zarrieß, Sina
%A Schlangen, David
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F zarriess-schlangen-2017-obtaining
%X We investigate object naming, which is an important sub-task of referring expression generation on real-world images. As opposed to mutually exclusive labels used in object recognition, object names are more flexible, subject to communicative preferences and semantically related to each other. Therefore, we investigate models of referential word meaning that link visual to lexical information which we assume to be given through distributional word embeddings. We present a model that learns individual predictors for object names that link visual and distributional aspects of word meaning during training. We show that this is particularly beneficial for zero-shot learning, as compared to projecting visual objects directly into the distributional space. In a standard object naming task, we find that different ways of combining lexical and visual information achieve very similar performance, though experiments on model combination suggest that they capture complementary aspects of referential meaning.
%R 10.18653/v1/P17-1023
%U https://aclanthology.org/P17-1023
%U https://doi.org/10.18653/v1/P17-1023
%P 243-254

Markdown (Informal)

[Obtaining referential word meanings from visual and distributional information: Experiments on object naming](https://aclanthology.org/P17-1023) (Zarrieß & Schlangen, ACL 2017)

• Obtaining referential word meanings from visual and distributional information: Experiments on object naming (Zarrieß & Schlangen, ACL 2017)

ACL

• Sina Zarrieß and David Schlangen. 2017. Obtaining referential word meanings from visual and distributional information: Experiments on object naming. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 243–254, Vancouver, Canada. Association for Computational Linguistics.