@inproceedings{sarti-etal-2021-looks,
    title = "That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models",
    author = "Sarti, Gabriele  and
      Brunato, Dominique  and
      Dell{'}Orletta, Felice",
    editor = "Chersoni, Emmanuele  and
      Hollenstein, Nora  and
      Jacobs, Cassandra  and
      Oseki, Yohei  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.cmcl-1.5/",
    doi = "10.18653/v1/2021.cmcl-1.5",
    pages = "48--60",
    abstract = "This paper investigates the relationship between two complementary perspectives in the human assessment of sentence complexity and how they are modeled in a neural language model (NLM). The first perspective takes into account multiple online behavioral metrics obtained from eye-tracking recordings. The second one concerns the offline perception of complexity measured by explicit human judgments. Using a broad spectrum of linguistic features modeling lexical, morpho-syntactic, and syntactic properties of sentences, we perform a comprehensive analysis of linguistic phenomena associated with the two complexity viewpoints and report similarities and differences. We then show the effectiveness of linguistic features when explicitly leveraged by a regression model for predicting sentence complexity and compare its results with the ones obtained by a fine-tuned neural language model. We finally probe the NLM{'}s linguistic competence before and after fine-tuning, highlighting how linguistic information encoded in representations changes when the model learns to predict complexity."
}