Logic Constrained Pointer Networks for Interpretable Textual Similarity
This work addresses the need for interpretable models in NLP tasks like semantic similarity, offering a domain-specific advancement with incremental improvements in alignment accuracy.
The paper tackles the problem of aligning sentence components for interpretable semantic textual similarity by introducing a pointer network model with sentinel gating, bidirectional loss, and logic constraints from ConceptNet and syntax. It achieves F1 scores of 97.73 and 96.32 on SemEval datasets, showing large improvements over existing solutions.
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