To Batch or Not to Batch? Comparing Batching and Curriculum Learning Strategies across Tasks and Datasets

Laura Burdick, Jonathan K. Kummerfeld and Rada Mihalcea
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Laura Burdick: Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Jonathan K. Kummerfeld: Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Rada Mihalcea: Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA

Mathematics, 2021, vol. 9, issue 18, 1-11

Abstract: Many natural language processing architectures are greatly affected by seemingly small design decisions, such as batching and curriculum learning (how the training data are ordered during training). In order to better understand the impact of these decisions, we present a systematic analysis of different curriculum learning strategies and different batching strategies. We consider multiple datasets for three tasks: text classification, sentence and phrase similarity, and part-of-speech tagging. Our experiments demonstrate that certain curriculum learning and batching decisions do increase performance substantially for some tasks.

Keywords: natural language processing; word embeddings; batching; word2vec; curriculum learning; text classification; phrase similarity; part-of-speech tagging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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