Font LDL 2020

This is a repository for our ACL 2020 paper Let Me Choose: From Verbal Context to Font Selection.

Requirements

python==3.6.9 and pip install -r requirements.txt

Data

You can find the Font dataset in the following repository: https://github.com/RiTUAL-UH/Font-prediction-dataset

Settings required for the Emoji model:

Emoji Model: In this model, we use the Deep-Moji pre-trained model (Felbo et al., 2017) to generate emoji vectors by encoding the text into 2304-dimensional feature vectors. Our implementation is based on the Hugging Face Torch-moji implementation. You can find emoji vectors for the Font dataset here.

Instructions for running the code

• pip install -r requirements.txt
• python -m nltk.downloader wordnet
• Download http://nlp.stanford.edu/data/glove.6B.zip and unzip glove.6B.100d.txt (part of glove.6B.zip) to EMBEDDINGS/glove.6B/glove.6B.100d.txt.
• Download http://sentiment.nrc.ca/lexicons-for-research/NRC-Sentiment-Emotion-Lexicons.zip, then unzip NRC-Sentiment-Emotion-Lexicons/NRC-Emotion-Lexicon-v0.92/* and NRC-Sentiment-Emotion-Lexicons/NRC-VAD-Lexicon/* to DATA/emotion_lexicon.
• Download http://saifmohammad.com/WebDocs/NRC-AffectIntensity-Lexicon.txt and copy NRC-AffectIntensity-Lexicon.txt to DATA/emotion_lexicon.
• In config.py select the model and configurations. base_model values are "glove", "bert_seq_classification", "emoji" and "NRCfeat". (For more information about the details of the models check out the ACL paper)
• Change train and test to True for training and testing respectively.
• Use python main.py for running the model.

Citation

If you use this code in your work, please cite our paper as follows:

@inproceedings{shirani2020font,
  title={Let Me Choose: From Verbal Context to Font Selection},
  author={Shirani, Amirreza and Dernoncourt, Franck and Echevarria, Jose and Asente, Paul and Lipka, Nedim and Solorio, Thamar},
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year={2020}
}