Hybrid and Collaborative Passage Reranking (2023)

Abstract: In passage retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages in the initial retrieval list. To tackle this problem, we propose a Hybrid and Collaborative Passage Reranking (HybRank) method, which leverages the substantial similarity measurements of upstream retrievers for passage collaboration and incorporates the lexical and semantic properties of sparse and dense retrievers for reranking. Besides, built on off-the-shelf retriever features, HybRank is a plug-in reranker capable of enhancing arbitrary passage lists including previously reranked ones. Extensive experiments demonstrate the stable improvements of performance over prevalent retrieval and reranking methods, and verify the effectiveness of the core components of HybRank.

## introduction information retrieval is a fundamental component within the field of natural language processing @xcite . retrieval aims to search a set of candidate documents from a large-scale corpus, and thus high recall retrieval with efficiency is required to cover more relevant documents as far as possible. traditionally, retrieval has been dominated by lexical methods like tf-idf and bm25 @xcite , which treat queries and documents as sparse bag-of-words vectors and match them in token-level. recently, neural networks have become prevalent to deal with information retrieval, where queries and documents are encoded into dense contextualized semantic vectors @xcite @xcite , and then retrieval is performed with highly optimized vector search algorithms @xcite . although numerous efforts have been dedicated to retrieval, the inherent efficiency requirement restricts the interaction between query and passage to a shallow level, leading to unsatisfactory retrieval results. thus, in typical reranking @xcite , query and passage are concatenated and fed into a transformer @xcite pre-trained on large corpus, to estimate a more fine-grained relevance score and further enhance the retrieval results with richer interaction. these methods consider each passage in isolation, ignoring the context of the retrieved passage list. some learning to rank @xcite and pseudo-relevance feedback @xcite ) methods utilize the ordinal relationship or listwise context of retrieved documents to further refine the retrieval. moreover, the necessity of integrating listwise context is confirmed in multi-stage recommendation systems @xcite . inspired by the success of listwise modeling and collaborative filtering @xcite in recommendation systems, we find that collaboration also exists among the passages in the retrieval list and has not been fully exploited. intuitively, for a specific query, a set of passages relevant to the query tend to describe the same entities, events and relations @xcite , while irrelevant ones outside of this set involve multifarious objects. therefore, a passage is more likely to be relevant with the query if most of other passages share similar content with it. similarities between passages can be naturally derived from retrievers, like bm25 scores in sparse foot_1 retrievers and dot product of embeddings in dense retrievers. in addition, the sparse and dense retrieval methods emphasize distinct linguistic aspects. sparse retrieval relies on lexical overlap while dense retrieval focuses on semantic and contextual relevance. several researchers have attempted to integrate the merits of these two types of methods. @xcite , @xcite and @xcite exploit the linear combination of these two types of retrieval scores. @xcite , @xcite and @xcite index smaller units in sentence, i.e., words or phrases, to obtain fine-grained similarity. @xcite and @xcite retrain dense retriever from scratch with the supervision of sparse signals. nevertheless, the linear score combination lacks sufficient interaction, indexing smaller units sacrifices efficiency due to tremendous amount of embeddings, while rebuilding of retrievers discards their origin ranking capability. to fully exploit the context of retrieved passages list and explore more sufficient ensemble of heterogeneous retriever, we propose a hybrid and collaborative passage reranking (hybrank) method, which leverages the collaboration within retrieved passages and incorporates diverse properties of retrievers for reranking. our method is a flexible plug-in reranker that can be applied to arbitrary passage lists, including those that have already been reranked by other methods. in this work, without loss of generality, we employ the two most representative types of retrievers: sparse and dense retriever. given a query and an initial retrieval list, we first extract similarities between them and a set of anchor texts via both the sparse and dense retrievers. we project and group them to form a set of hybrid and collaborative sequences, each corresponding to a query or passage. afterwards, the relevance scores between the query and these passages are evaluated in the light of these sequences. extensive experiments demonstrate the consistent performance improvement brought by hyb-rank over passage lists from prevalent retrievers and strong rerankers. we elaborate ablation studies on the collaborative information, feature hybrid, anchor-wise interaction and the number of anchor passages, verifying the impact and indispensability of these components in hybrank. ## method in mainstream information retrieval systems, the first-stage retrieval is designed to fetch a coarse candidate list from a large corpus c. inevitably, false positives, i.e., irrelevant passages in the re-trieval list, are returned in the first-stage retrieval. to improve the precision of retrieval systems, the follow-up procedure reranking aims to distinguish the relevant passages from others in the retrieval list. this paper focuses on the reranking stage. formally, given a query q and an initial passage list @xmath0, p 2 , . . . , p n ] from upstream retriever, the reranking task is to reorder the passage list by reassigning scores @xmath1, s 2 , . . . , s n ] for each of these passages. we denote positive passages in the list as p + and negative ones as p -. in this section, we will present the details of hybrank. the pipeline of hybrank is illustrated in figure 1 . ## conclusion we introduce hybrank, a hybrid and collaborative passage reranking method. hybrank extracts the similarities between texts via off-the-shelf retrievers to form hybrid and collaborative sequences as the representations of query and passages. efficient reranking is based on these sequences which incorporate the lexical and semantic properties of sparse and dense retrievers. extensive experiments confirm the effectiveness of hybrank upon arbitrary passage list. elaborated ablation studies investigate the impact of core components in hybrank. we hope our work could provide inspiration for researchers in the field of information retrieval, and steer more exploration on collaboration and correlation between texts.