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README.md

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-Edit this `README.md` markdown file to author your organization card.
+This repository is Cambridge University NLP's submission to the 2023 BabyLM Challenge (CoNLL workshop). 
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+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. 
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+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.
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+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.