Grounding learning of modifier dynamics: An application to color naming

Xudong Han, Philip Schulz, Trevor Cohn


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.
Anthology ID:
D19-1158
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1488–1493
Language:
URL:
https://aclanthology.org/D19-1158
DOI:
10.18653/v1/D19-1158
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/D19-1158.pdf
Code
 HanXudong/GLoM