RussianNLP/EyeWino

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The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 9cc34a8
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Citation text from the card

@inproceedings{kozlova-etal-2024-transformer,
    title = "Transformer Attention vs Human Attention in Anaphora Resolution",
    author = "Kozlova, Anastasia  and
      Akhmetgareeva, Albina  and
      Khanova, Aigul  and
      Kudriavtsev, Semen  and
      Fenogenova, Alena",
    editor = "Kuribayashi, Tatsuki  and
      Rambelli, Giulia  and
      Takmaz, Ece  and
      Wicke, Philipp  and
      Oseki, Yohei",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.cmcl-1.10",
    pages = "109--122",
    abstract = "Motivated by human cognitive processes, attention mechanism within transformer architecture has been developed to assist neural networks in allocating focus to specific aspects within input data. Despite claims regarding the interpretability achieved by attention mechanisms, the extent of correlation and similarity between machine and human attention remains a subject requiring further investigation.In this paper, we conduct a quantitative analysis of human attention compared to neural attention mechanisms in the context of the anaphora resolution task. We collect an eye-tracking dataset based on the Winograd schema challenge task for the Russian language. Leveraging this dataset, we conduct an extensive analysis of the correlations between human and

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dataset_licence({ "id": "RussianNLP/EyeWino" })
signal: "permissive"
stated_licence: "mit"
sources: 0