Logic Constrained Pointer Networks for Interpretable Textual Similarity

TL;DR

This paper presents a novel logic-constrained pointer network model that improves interpretability and accuracy in semantic textual similarity tasks by aligning sentence components with external knowledge integration.

Contribution

The paper introduces a new pointer network model with logic constraints and bidirectional alignment for more interpretable and accurate semantic similarity measurement.

Findings

Achieved F1 scores of 97.73 and 96.32 on SemEval datasets.

Significant improvements over existing alignment solutions.

Enhanced interpretability through structured alignment and external knowledge.

Abstract

Systematically discovering semantic relationships in text is an important and extensively studied area in Natural Language Processing, with various tasks such as entailment, semantic similarity, etc. Decomposability of sentence-level scores via subsequence alignments has been proposed as a way to make models more interpretable. We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity. In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented using BERT. We improve this base model with a loss function to equally penalize misalignments in both sentences, ensuring the alignments are bidirectional. Finally, to guide the network with structured external knowledge, we introduce first-order logic constraints based on ConceptNet and syntactic knowledge. The model achieves an F1 score of 97.73 and 96.32 on the benchmark SemEval datasets for the chunk alignment task, showing large improvements over the existing solutions. Source code is available at https://github.com/manishb89/interpretable_sentence_similarity