[Paper Review] Recursive Neural Networks for Learning Logical Semantics.

Samuel R. Bowman, Christopher Potts+1 more|arXiv (Cornell University)|Jun 6, 2014

Topic ModelingComputer Science21 references22 citations

TL;DR

This paper evaluates whether recursive neural networks—specifically plain RNNs and recursive neural tensor networks (RNTNs)—can learn compositional logical semantics for natural language inference. Using controlled datasets generated from a logical grammar, it finds that RNTNs generalize well across entailment, contradiction, and quantified sentences, while plain RNNs achieve only mixed performance, indicating RNTNs are better suited for learning representations supporting logical deduction.

ABSTRACT

Supervised recursive neural network models (RNNs) for sentence meaning have been successful in an array of sophisticated language tasks, but it remains an open question whether they can learn compositional semantic grammars that support logical deduction. We address this question directly by for the first time evaluating whether each of two classes of neural model — plain RNNs and recursive neural tensor networks (RNTNs) — can correctly learn relationships such as entailment and contradiction between pairs of sentences, where we have generated controlled data sets of sentences from a logical grammar. Our first experiment evaluates whether these models can learn the basic algebra of logical relations involved. Our second and third experiments extend this evaluation to complex recursive structures and sentences involving quantification. We find that the plain RNN achieves only mixed results on all three experiments, whereas the stronger RNTN model generalizes well in every setting and appears capable of learning suitable representations for natural language logical inference.

Motivation & Objective

• To investigate whether supervised recursive neural networks can learn compositional semantic grammars capable of supporting logical deduction.
• To assess the ability of neural models to capture logical relations such as entailment and contradiction in sentence pairs.
• To evaluate model generalization on increasingly complex recursive structures and sentences involving quantification.
• To compare the performance of plain RNNs and recursive neural tensor networks (RNTNs) in learning logical semantics.

Proposed method

• The authors generate controlled datasets of sentence pairs using a formal logical grammar to define entailment and contradiction relationships.
• They train both plain RNNs and recursive neural tensor networks (RNTNs) on these datasets to learn sentence representations.
• The models are evaluated on their ability to predict logical relations between sentence pairs, including basic logical algebra and complex recursive structures.
• The evaluation includes sentences with quantifiers, testing the models' capacity to generalize beyond simple patterns.
• The RNTN model uses tensor-based composition to learn nonlinear interactions between word representations, enhancing its representational capacity.
• Performance is measured via accuracy in predicting logical relations across multiple experimental settings.

Experimental results

Research questions

• RQ1 Can plain RNNs learn the basic algebra of logical relations such as entailment and contradiction in sentence pairs?
• RQ2 Can RNTNs generalize across complex recursive sentence structures while maintaining accurate logical inference?
• RQ3 How do both models perform on sentences involving quantification, which require deeper compositional understanding?
• RQ4 Does the inclusion of tensor-based composition in RNTNs lead to better generalization in logical reasoning tasks compared to plain RNNs?

Key findings

• The plain RNN achieves only mixed results across all three experiments, indicating limited generalization on logical inference tasks.
• The RNTN model generalizes well in every experimental setting, including those with complex recursive structures and quantified sentences.
• The RNTN demonstrates superior performance in learning representations suitable for natural language logical inference.
• The results suggest that RNTNs are capable of learning compositional semantic grammars that support logical deduction.
• The stronger representational capacity of RNTNs, due to tensor-based composition, enables better learning of logical relations than plain RNNs.