Few-Shot Issue Report Classification with Adapters

Author:

Ebrahim Fahad1ORCID,Joy Mike1ORCID

Affiliation:

1. Computer Science, University of Warwick, Warwickshire, England, United Kingdom

Publisher

ACM

Reference15 articles.

1. AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters

2. Few-Shot Learning for Issue Report Classification

3. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning. PMLR, 2790--2799.

4. Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hérve Jégou, and Tomas Mikolov. 2016. Fasttext. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651 (2016).

5. Rafael Kallis, Giuseppe Colavito, Ali Al-Kaswan, Luca Pascarella, Oscar Chaparro, and Pooja Rani. 2024. The NLBSE'24 Tool Competition. In Proceedings of The 3rd International Workshop on Natural Language-based Software Engineering (NLBSE'24).

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