Leveraging Symbolic Knowledge Bases for Commonsense Natural Language Inference Using Pattern Theory.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Commonsense Natural Language Inference (CNLI) tasks require substantial labeled data for effective transfer learning.
• Existing methods for CNLI model adaptation are data-intensive, limiting their application in low-resource scenarios.

Purpose of the Study:

• To develop a novel framework that reduces the reliance on annotated training data for CNLI tasks.
• To leverage symbolic knowledge bases for knowledge distillation in a teacher-student model for CNLI.

Main Methods:

• A hybrid symbolic-neural reasoning framework is proposed, using ConceptNet as a symbolic teacher and a CNLI model as a student.
• Weakly labeled data is generated from unlabeled data using an abductive reasoning framework based on Grenander's pattern theory.
• Knowledge distillation is applied using weakly labeled data and a small fraction of labeled data for CNLI model transfer learning.

Main Results:

• The approach achieves 63% of top performance with no labeled data and 72% with 1,000 labeled samples.
• The pattern theory framework demonstrates strong standalone inference capabilities, outperforming models like GPT and BERT on OpenBookQA.
• The framework generalizes to unsupervised and semi-supervised learning settings, outperforming existing baselines.

Conclusions:

• The proposed hybrid distillation method significantly reduces the need for labeled data in CNLI tasks.
• The framework offers a flexible and effective approach for knowledge transfer in NLP, adaptable to various downstream tasks.
• The method enhances model explainability through generated interpretations, providing insights into the reasoning process.