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Learning to Generate Compositional Color Descriptions
2016
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
The production of color language is essential for grounded language generation. Color descriptions have many challenging properties: they can be vague, compositionally complex, and denotationally rich. We present an effective approach to generating color descriptions using recurrent neural networks and a Fouriertransformed color representation. Our model outperforms previous work on a conditional language modeling task over a large corpus of naturalistic color descriptions. In addition, probing
doi:10.18653/v1/d16-1243
dblp:conf/emnlp/MonroeGP16
fatcat:nlgku4tlrbgbpp7dwz3zq74twy