Cesar Gonzalez-Gutierrez


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Analyzing Text Representations by Measuring Task Alignment
Cesar Gonzalez-Gutierrez | Audi Primadhanty | Francesco Cazzaro | Ariadna Quattoni
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Textual representations based on pre-trained language models are key, especially in few-shot learning scenarios. What makes a representation good for text classification? Is it due to the geometric properties of the space or because it is well aligned with the task? We hypothesize the second claim. To test it, we develop a task alignment score based on hierarchical clustering that measures alignment at different levels of granularity. Our experiments on text classification validate our hypothesis by showing that task alignment can explain the classification performance of a given representation.