Transformer Based Language Models for Similar Text Retrieval and Ranking

Abstract

Most approaches for similar text retrieval and ranking with long natural language queries rely at some level on queries and responses having words in common with each other. Recent applications of transformer-based neural language models to text retrieval and ranking problems have been very promising, but still involve a two-step process in which result candidates are first obtained through bag-of-words-based approaches, and then reranked by a neural transformer. In this paper, we introduce novel approaches for effectively applying neural transformer models to similar text retrieval and ranking without an initial bag-of-words-based step. By eliminating the bag-of-words-based step, our approach is able to accurately retrieve and rank results even when they have no non-stopwords in common with the query. We accomplish this by using bidirectional encoder representations from transformers (BERT) to create vectorized representations of sentence-length texts, along with a vector nearest neighbor search index. We demonstrate both supervised and unsupervised means of using BERT to accomplish this task.

5 pages, 2 figures

References in corpus (6)

• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• Deeper Text Understanding for IR with Contextual Neural Language Modeling
• Passage Re-ranking with BERT
• Simple Applications of BERT for Ad Hoc Document Retrieval
• MatchZoo: A Toolkit for Deep Text Matching
• Investigating the Successes and Failures of BERT for Passage Re-Ranking