@inproceedings{ballapuram-2020-lmml,
    title = "{LMML} at {S}em{E}val-2020 Task 7: {S}iamese Transformers for Rating Humor in Edited News Headlines",
    author = "Ballapuram, Pramodith",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.134/",
    doi = "10.18653/v1/2020.semeval-1.134",
    pages = "1026--1032",
    abstract = "This paper contains a description of my solution to the problem statement of SemEval 2020: Assessing the Funniness of Edited News Headlines. I propose a Siamese Transformer based approach, coupled with a Global Attention mechanism that makes use of contextual embeddings and focus words, to generate important features that are fed to a 2 layer perceptron to rate the funniness of the edited headline. I detail various experiments to show the performance of the system. The proposed approach outperforms a baseline Bi-LSTM architecture and finished 5th (out of 49 teams) in sub-task 1 and 4th (out of 32 teams) in sub-task 2 of the competition and was the best non-ensemble model in both tasks."
}