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2021.cmcl-1.5.pdf
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🔗 Source: aclanthology.org
📊 Size: 935 KB
👤 Author: Gabriele Sarti ; Dominique Brunato ; Felice Dell'Orletta
⬇️ Downloads: 108

Summary

It analyzes linguistic features associated with sentence-level structural phenomena and their correlation with offline and online complexity metrics, including eye-tracking data and human perception of complexity. The study evaluates the performance of models using explicit features or contextualized word embeddings and shows that both obtain comparable results when predicting complexity scores. It also probes the neural language model's linguistic competence before and after fine-tuning, highlighting how linguistic information encoded in representations changes when the model learns to predict complexity. The results suggest that the model's competencies during training are interpretable from a linguistic perspective and are related to its predictive capabilities for complexity assessment.

Description

It analyzes linguistic features associated with sentence-level structural phenomena and their correlation with offline and online complexity metrics, including...

Technical Information

  • File Format: PDF
  • File Size: 935 KB
  • Pages: 13
  • Language: EN
  • Author: Gabriele Sarti ; Dominique Brunato ; Felice Dell'Orletta
  • Total Downloads: 108
  • Last Updated: 1 week ago

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