Topic-DPR: Topic-based Prompts for Dense Passage Retrieval
Qingfa Xiao, Shuangyin Li, Lei Chen · 2023
Prompt-based learning's efficacy across numerous natural language processing tasks has led to its integration into dense passage retrieval.Prior research has mainly focused on enhancing the semantic understanding of pre-trained language models by optimizing a single vector as a continuous prompt.This approach, however, leads to a semantic space collapse; identical semantic information seeps into all representations, causing their distributions to converge in a restricted region.This hinders differentiation between relevant and irrelevant passages during dense retrieval.To tackle this issue, we present Topic-DPR, a dense passage retrieval model that uses topic-based prompts.Unlike the single prompt method, multiple topic-based prompts are established over a probabilistic simplex and optimized simultaneously through contrastive learning.This encourages representations to align with their topic distributions, improving space uniformity.Furthermore, we introduce a novel positive and negative sampling strategy, leveraging semi-structured data to boost dense retrieval efficiency.Experimental results from two datasets affirm that our method surpasses previous state-of-the-art retrieval techniques.