Learning Semantic Textual Similarity from Conversations
Topics: AI (Deep Learning), LLMO / GEO, Semantic Search
This Google research introduces a method to learn sentence-level semantic similarity using large-scale conversational data. By training models to predict conversational input-response pairs (e.g., Reddit comments), the system learns useful sentence embeddings. These embeddings perform competitively on standard semantic similarity benchmarks, and performance is further enhanced by incorporating a multitask learning approach that includes Natural Language Inference (NLI) data. The study demonstrates state-of-the-art performance among neural models for semantic textual similarity tasks.