tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection
Nicole Peinelt, Dong Nguyen, Maria Liakata · 2020
Semantic similarity detection is a fundamental task in natural language understanding.Adding topic information has been useful for previous feature-engineered semantic similarity models as well as neural models for other tasks.There is currently no standard way of combining topics with pretrained contextual representations such as BERT.We propose a novel topic-informed BERT-based architecture for pairwise semantic similarity detection and show that our model improves performance over strong neural baselines across a variety of English language datasets.We find that the addition of topics to BERT helps particularly with resolving domain-specific cases.