Antonio Laverghetta Jr.


Curriculum vitae



Quantitative Analysis

Wesleyan University



Towards a task-agnostic model of difficulty estimation for supervised learning tasks


Conference paper


Antonio Laverghetta Jr, Jamshidbek Mirzakhalov, John Licato
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, 2020, pp. 16--23

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Cite

APA   Click to copy
Laverghetta Jr, A., Mirzakhalov, J., & Licato, J. (2020). Towards a task-agnostic model of difficulty estimation for supervised learning tasks. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop (pp. 16–23).


Chicago/Turabian   Click to copy
Laverghetta Jr, Antonio, Jamshidbek Mirzakhalov, and John Licato. “Towards a Task-Agnostic Model of Difficulty Estimation for Supervised Learning Tasks.” In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, 16–23, 2020.


MLA   Click to copy
Laverghetta Jr, Antonio, et al. “Towards a Task-Agnostic Model of Difficulty Estimation for Supervised Learning Tasks.” Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, 2020, pp. 16–23.


BibTeX   Click to copy

@inproceedings{laverghetta2020a,
  title = {Towards a task-agnostic model of difficulty estimation for supervised learning tasks},
  year = {2020},
  pages = {16--23},
  author = {Laverghetta Jr, Antonio and Mirzakhalov, Jamshidbek and Licato, John},
  booktitle = {Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop}
}


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