Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech.

Area of Science:

• Cognitive Science
• Computational Linguistics
• Artificial Intelligence

Background:

• Distributional learning mechanisms are proposed for semantic knowledge acquisition.
• Deep learning models face criticism for lacking abstract and structured knowledge capabilities.

Purpose of the Study:

• To investigate if recurrent neural networks can learn abstract and structured semantic knowledge from naturalistic speech.
• To compare the semantic knowledge acquired by different neural network architectures.

Main Methods:

• Trained Simple Recurrent Network (SRN) and Long Short-Term Memory (LSTM) models on a 5-million-word corpus of child-directed speech (ages 0-3).
• Assessed acquired semantic knowledge by analyzing internal representations and similarity structures.
• Compared performance with a non-recurrent Skip-gram model, a state-of-the-art machine learning approach.

Main Results:

• Both SRN and LSTM models learned abstract grammatical and semantic features for word sequence prediction.
• Evidence of emergent categorical and hierarchical semantic structures was found in both recurrent models.
• LSTM outperformed SRN quantitatively, while Skip-gram showed similar performance but with more thematic word representations.

Conclusions:

• Recurrent neural networks can acquire abstract and structured semantic knowledge from naturalistic language input.
• Learned representations offer insights into the emergence of semantic systems in child language acquisition.
• The findings challenge limitations previously attributed to deep learning in acquiring abstract knowledge.