@inproceedings{zhang-etal-2020-ecnu,
    title = "{ECNU} at {S}em{E}val-2020 Task 7: Assessing Humor in Edited News Headlines Using {B}i{LSTM} with Attention",
    author = "Zhang, Tiantian  and
      Chen, Zhixuan  and
      Lan, Man",
    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/ingest-emnlp/2020.semeval-1.129/",
    doi = "10.18653/v1/2020.semeval-1.129",
    pages = "995--1000",
    abstract = "In this paper we describe our system submitted to SemEval 2020 Task 7: ``Assessing Humor in Edited News Headlines''. We participated in all subtasks, in which the main goal is to predict the mean funniness of the edited headline given the original and the edited headline. Our system involves two similar sub-networks, which generate vector representations for the original and edited headlines respectively. And then we do a subtract operation of the outputs from two sub-networks to predict the funniness of the edited headline."
}