@inproceedings{almeida-etal-2021-c3sl,
    title = "{C}3{SL} at {S}em{E}val-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings",
    author = "Almeida, Raul  and
      Tissot, Hegler  and
      Fabro, Marcos Didonet Del",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.semeval-1.88/",
    doi = "10.18653/v1/2021.semeval-1.88",
    pages = "683--687",
    abstract = "We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the embeddings into a simpler vector used as input to a neural network, the latter for predicting the complexity of words and expressions. Results show that the pre-trained sentence embeddings are not able to capture lexical complexity from the language when applied in cross-domain applications."
}