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

Mathematics

Laura Burdick¹  Rada Mihalcea¹  Jonathan K. Kummerfeld¹ 

[1] Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA;

关键词: natural language processing;    word embeddings;    batching;    word2vec;    curriculum learning;    text classification;   

DOI  :  10.3390/math9182234

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown