2023 UnifierAUnifiedRetrieverforLarg

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Subject Headings: RAG Algorithm.

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Abstract

Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semantic encoding space. According to the encoding space, recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. These two paradigms unveil the PLMs' representation capability in different granularities, i.e., global sequence-level compression and local word-level contexts, respectively. Inspired by their complementary global-local contextualization and distinct representing views, we propose a new learning framework, Unifier, which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability. Experiments on passage retrieval benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme is further presented with even better retrieval quality. We lastly evaluate the model on BEIR benchmark to verify its transferability.

KEYWORDS

deep representation learning; pre-trained language model; neural encoder; hybrid retrieval

1 INTRODUCTION

Large-scale retrieval aims to efficiently fetch all relevant documents for a given query from a large-scale collection with millions or billions of entries [1]. It plays indispensable roles as a prerequisite for a broad spectrum of downstream tasks, e.g., information retrieval [2], open-domain question answering [3]. To make online large-scale retrieval possible, the common practice is to represent queries and documents by an encoder in a Siamese manner (i.e., Bi-Encoder, BE) [39]. So, its success depends heavily on a powerful encoder by effective representation learning.

Advanced by pre-trained language models (PLM), e.g., BERT [9], recent works propose to learn PLM-based encoders for large-scale retrieval, which are coarsely grouped into two paradigms in light of their encoding spaces with different focuses of representation granularity. That is, dense-vector encoding methods leverage sequence-level compressive representations that embedded into dense semantic space [14, 24, 51, 54], whereas lexicon-based encoding methods make the best of word-level contextual representations by considering either high concurrence [36] or coordinate terms [12] in PLMs. To gather the powers of both worlds, some pioneering works pro- pose hybrid methods to achieve a sweet point between dense-vector and lexicon-based methods for better retrieval quality. They focus on interactions of predicted scores between the two paradigms.

Nonetheless, such surface interactions – score aggregations [25], direct co-training [16], and logits distillations [5] – cannot fully exploit the benefits of the two paradigms – regardless of their complementary contextual features and distinct representation views. Specifically, as for contextual features, the dense-vector models focus more on sequence-level global embeddings against information bottleneck [13, 14, 31], whereas the lexicon-based models focus on word-level local contextual embeddings for precise lexicon-weighting [10, 11, 36]. Aligning the two retrieval paradigms more closely is likely to benefit each other since global-local contexts are proven complementary in general representation learning [1, 44]. As for representing views, relying on distinct encoding spaces, the two retrieval paradigms are proven to provide different views in terms of query-document relevance [15, 16, 25]. Such a sort of ‘dual views’ has been proven pivotal in many previous cooperative learning works [4, 17, 18, 27], which provides a great opportunity to bridge the two retrieval paradigms. Consequently, without any in-depth interactions, neither the single (dense/lexicon) nor the hybrid retrieval model can be optimal.

Motivated by the above, we propose a brand-new learning framework, Unified Retriever (Unifier), for in-depth mutual benefits of both dense-vector and lexicon-based retrieval. On the one hand, we present a neural encoder with dual representing modules for Unifier, which is compatible with both retrieval paradigms. Built upon an underlying-tied contextualization that empowers consistent semantics sharing, a local-enhanced sequence representation module is presented to learn a dense-vector representation model. Meantime, a global-aware lexicon weighting module considering both the global- and local-context is proposed for a lexicon-based representation. On the other hand, we propose a new self-learning strategy, called dual-consistency learning, upon our unified encoder. Besides a basic contrastive learning objective, we first exploit the unified dual representing modules by mining diverse hard negatives for self-adversarial within the Unifier. Furthermore, we present a self-regularization method based on list-wise agreements from the dual views for better consistency and generalization.

After being trained, Unifier performs large-scale retrieval via either its lexicon representation by efficient inverted index or dense vectors by parallelizable dot-product. Moreover, empowered by our Unifier, we present a fast yet effective retrieval scheme, uni-retrieval, to gather the powers of both worlds, where the lexicon retrieval is followed by a candidate-constrained dense scoring. Empirically, we evaluate Unifier on both passage retrieval benchmarks to check its effectiveness and the BEIR benchmark [48] with twelve datasets (e.g., Natural Questions, HotpotQA) to verify its transferability.

2 RELATED WORK

PLM-based Retriever. Built upon PLMs, recent works propose to learn encoders for large-scale retrieval, which are coarsely grouped into two paradigms in light of their encoding spaces with different focuses of representation granularity: (i) Dense-vector encoding methods directly represent a document/query as a low-dimension sequence-level dense vector 𝒖 ∈ R𝑒 (𝑒 is embedding size and usually small, e.g., 768). And the relevance score between a document and a query is calculated by dot-product or cosine similarity [14, 24, 51, 54]. (ii) Lexicon-based encoding methods make the best of word-level contextualization by considering either high concurence [36] or coordinate terms [12] in PLMs. It first weights all vocabulary lexicons for each word of a document/query based on the contexts, leading to a high-dimension sparse vector 𝒗 ∈ R|V| (|V| is the vocabulary size and usually large, e.g., 30k). The text is then denoted by aggregating over all the lexicons in a sparse manner. Lastly, the relevance is calculated by lexical-based matching metrics (e.g., BM25 [42]). In contrast, we unify the two paradigms into one sophisticated encoder for better consistency within PLMs, leading to complementary information and superior performance.

Hybrid Retriever. Some works propose to bridge the gap between dense and lexicon for a sweet spot between performance and efficiency. A direct method is to aggregate scores of the two paradigms [25], but resulting in standalone learning and sub-optimal quality. Similar to our work, CLEAR [16] uses a dense-vector model to complement the lexicon-based BM25 model, but without feature interactions and sophisticated learning. Sharing inspiration with our uni-retrieval scheme, COIL [15] equips a simple lexicon-based retrieval with dense operations over word-level contextual embeddings. Unifier differs in not only our lexicon representations jointly learned for in-depth mutual benefits but also sequence-level dense operations involved for memory-/computation-efficiency. Lastly, SPARC [26] distills ranking orders from a lexicon model (BM25) into a dense model as a companion of the original dense vector, which is distinct to our motivation.

Bottleneck-based Learning. For neural designs,our encoder is similar to several recent representation learning works, e.g., SEED- Encoder [31]. Condenser [13], coCondenser [14], and DiffCSE [6], but they focus on the bottleneck of sequence-level dense vectors. For example, SEED-Encoder, Condenser, and CoCondenser enhance their dense capabilities by emphasizing the sequence-level bot- tleneck vector and weakening the word-level language modeling heads, while DiffCSE makes the learned sentence embedding sensi- tive to the difference between the original sentence and an edited sentence by a word-level discriminator. With distinct motivations and targets, we fully exploit both the dense-vector bottleneck and the word-level representation learning in a PLM for their mutual benefits. These are on the basis of not only the shared neural mod- ules but also structure-facilitated self-learning strategies (see the next section). But, as discussed in our experiments, our model can still benefit from these prior works via parameter initializations.

Instance-dependent Prompt. Our model also shares high-level inspiration with recent instance-dependent prompt learning meth- ods [22, 50]. They introduce a trainable component to generate prompts based on each input example. Such generated prompts can provide complementary features to the original input for a better prediction quality. Analogously, our sequence-level dense vector can be seen as a sort of ‘soft-prompt’ for the sparse lexicon-based representation module, resulting in the superiority of our lexicon- based retrieval, which will be discussed in experiments. In addition, the ‘soft-prompt’ in our Unifier also serves as crucial outputs in a unified retrieval system.

Reranker-taught Retriever. Distilling the scores from a reranker into a retriever is proven promising [10, 19, 20] . In light of this, recent works propose to jointly optimize a retriever and a reranker: RocketQAv2 [41] is proposed to achieve their agreements with reranker-filtered hard negatives, while AR2 [56] is to learn them in an adversarial fashion where the retriever is regarded as a generator and the reranker as a discriminator. In contrast to reranker-retriever co-training, we resort to in-depth sharing from the bottom (i.e., features) to the top (i.e., self-learning) merely within a retriever, with no need for extra overheads of reranker training. Meantime, our unified structure also uniquely enables it to learn from more diverse hard negatives mined by its dual representing modules.

3 METHODOLOGY

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2023 UnifierAUnifiedRetrieverforLargDaxin Jiang
Kai Zhang
Tao Shen
Xiubo Geng
Chongyang Tao
Can Xu
Guodong Long		Unifier: A Unified Retriever for Large-scale Retrieval10.1145/3580305.35999272023
  1. ↑ An entry can be passage, document, etc., and we take document for demonstrations.
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