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Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech.
Philip A Huebner1, Jon A Willits2
1Interdepartmental Neuroscience Graduate Program, University of California, Riverside, Riverside, CA, United States.
Frontiers in Psychology
|March 10, 2018
Summary
Recurrent neural networks trained on child-directed speech learn abstract semantic knowledge, challenging prior criticisms of deep learning models for abstract knowledge acquisition.
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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.