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Self-StrAE at SemEval-2024 Task 1: Making Self-Structuring AutoEncoders Learn More With Less
This paper presents two simple improvements to the Self-Structuring AutoEncoder (Self-StrAE).
On the effect of curriculum learning with developmental data for grammar acquisition
Using BabyBERTa as a probe, we find that grammar acquisition is largely driven by exposure to speech data, and in particular through exposure to two of the BabyLM training corpora: AO-Childes and Open Subtitles.
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured representations, enables effective learning of multi-level representations.
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