@inproceedings{han-etal-2019-grounding,
    title = "Grounding learning of modifier dynamics: An application to color naming",
    author = "Han, Xudong  and
      Schulz, Philip  and
      Cohn, Trevor",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "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 = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/D19-1158/",
    doi = "10.18653/v1/D19-1158",
    pages = "1488--1493",
    abstract = "Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as {\textquotedblleft}light blue{\textquotedblright}. 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."
}