Grounding learning of modifier dynamics: An application to color naming
This work addresses a subtask in natural language understanding for applications involving color naming, but it is incremental as it builds on existing additive models.
The paper tackled the problem of understanding modified color expressions like 'dirty blue' by developing models that learn complex transformations in RGB and HSV color spaces, with a hard ensemble model achieving significant and consistent improvements over the state-of-the-art baseline.
Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as 'dirty 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.