Neural Architecture Search for Sentence Classification with BERT

• URL: http://arxiv.org/abs/2403.18547v1
• Date: Wed, 27 Mar 2024 13:25:43 GMT
• Title: Neural Architecture Search for Sentence Classification with BERT
• Authors: Philip Kenneweg, Sarah Schröder, Barbara Hammer,
• Abstract summary: We perform an AutoML search to find architectures that outperform the current single layer at only a small compute cost. We validate our classification architecture on a variety of NLP benchmarks from the GLUE dataset.
• Score: 4.862490782515929
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Pre training of language models on large text corpora is common practice in Natural Language Processing. Following, fine tuning of these models is performed to achieve the best results on a variety of tasks. In this paper we question the common practice of only adding a single output layer as a classification head on top of the network. We perform an AutoML search to find architectures that outperform the current single layer at only a small compute cost. We validate our classification architecture on a variety of NLP benchmarks from the GLUE dataset.

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