README.md

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title: README
emoji: 📈
colorFrom: indigo
colorTo: blue
sdk: static
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license: mit
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This repository is Cambridge University NLP's submission to the 2023 BabyLM Challenge (CoNLL workshop). 

Our approach experiments with the following three variants of cognitively-motivated curriculum learning and analyze their effect on the performance of the model on linguistic evaluation tasks. 

1. **vocabulary curriculum** we analyze methods for constraining the vocabulary in the early stages of training to simulate cognitively more plausible learning curves.
2. **data curriculum** we vary the order of the training instances based on i) infant-inspired expectations and ii) the learning behaviour of the model
3. **objective curriculum** we explore different variations of combining the conventional masked language modelling task with a more coarse-grained word class prediction task to reinforce linguistic generalization capabilities.

Overall, we find that various curriculum learning settings outperform our baseline in linguistic tasks. We moreover find that careful selection of model architecture, and training hyper-parameters yield substantial improvements over the default baselines provided by the BabyLM challenge.