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Neural Fuzzy Logic Reasoning for Natural Language Inference
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- Author / Creator
- Wu, Zijun
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Natural language inference, also known as NLI, aims to determine the logical relationship between two sentences, such as Entailment, Contradiction, and Neutral. NLI is important to natural language processing, because it involves logical reasoning and is a key problem in artificial intelligence. In recent years, deep learning models have become a prevailing approach to NLI. Those approaches can achieve high performance, but lack interpretability and explainability.
In this work, we propose an Explainable Phrasal Reasoning (EPR) approach to address the explainability for NLI by weakly supervised logical reasoning. The system includes three main components. It first detects phrases as the semantic unit and aligns corresponding phrases. Then, it predicts the NLI label for the aligned phrases, and induces the sentence label by fuzzy logic formulas. Our EPR is almost everywhere differentiable and thus the system can be trained end-to-end in a weakly supervised manner. We annotated a corpus and developed a set of metrics to evaluate phrasal reasoning. Results show that our EPR yields much more meaningful explanations in terms of F scores than previous studies. To the best of our knowledge, we are the first to develop a weakly supervised phrasal reasoning model for the NLI task.
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- Graduation date
- Fall 2022
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.