Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025)

Abstract: Generative Information Retrieval is an emerging retrieval paradigm that exhibits remarkable performance in monolingual scenarios. However, applying these methods to multilingual retrieval still encounters two primary challenges, cross-lingual identifier misalignment and identifier inflation. To address these limitations, we propose Multilingual Generative Retrieval via Cross-lingual Semantic Compression (MGR-CSC), a novel framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space, and we propose a dynamic multi-step constrained decoding strategy during retrieval. MGR-CSC improves cross-lingual alignment by assigning consistent identifiers and enhances decoding efficiency by reducing redundancy. Experiments demonstrate that MGR-CSC achieves outstanding retrieval accuracy, improving by 6.83% on mMarco100k and 4.77% on mNQ320k, while reducing document identifiers length by 74.51% and 78.2%, respectively. We publicly release our dataset and code at https://github.com/simengggg/MGR-CSC

## introduction multilingual information retrieval (mir) serves as a critical component in natural language processing, particularly in applications such as crossborder e-commerce @xcite and crosslingual search systems @xcite . the core need lies in developing models that can effectively process multilingual queries and retrieve relevant documents across different languages @xcite . traditional translation-based approaches compromise retrieval quality due to error propagation in machine translation pipelines @xcite . recently multilingual pre-trained language models (plms) @xcite 2019) demonstrate improved performance by encoding cross-lingual content into joint semantic spaces @xcite . however, precise cross-lingual alignment and end-to-end semantic matching remain significant challenges for pervious methods. generative information retrieval (gir) offers a paradigm shift by leveraging the model's parametric memory to store documents and directly generates document identifiers (docids) @xcite @xcite . this approach leverages generative plms to learn and encode direct associations between documents and their unique docids. unlike previous paradigms, gir offers an alternative end-to-end framework by utilizing generative plms to directly map queries to relevant docids. this generative approach inherently addresses the mentioned challenges by learning direct associations between multilingual queries and docids. however, the application of gir to multilingual scenarios confronts two fundamental limitations: (i) cross-lingual identifier misalignment. existing docids are constructed using languageindependent encoding schemes, creating isolated semantic mapping spaces for each language. as shown in figure 1 (a), queries in a specific language lead the model to preferentially generate docids for documents in the same language, such as english and french. this phenomenon is a direct result of cross-lingual identifier misalignment, which consequently hinders the transfer of multilingual knowledge through shared latent document-level representations. (ii) multilingual identifier inflation. the number of existing docids increases manifold as the number of languages grows in multilingual gir. as shown in figure 1 (a), keywordbased methods assign different atom ids to semantically equivalent keywords across languages. for instance, the english word largest might be assigned atom id 75, while its french counterpart maximum is assigned atom id 366, despite their semantic equivalence. the combinatorial expansion of unique docids in multilingual documents intensifies this challenge, posing significant hurdles for memory efficiency and runtime performance in autoregressive decoding architectures. therefore, a semantically consistent docid is essential for cross-lingual alignment and efficient decoding. in this paper, we present multilingual generative retrieval via cross-lingual semantic compression (mgr-csc), a novel framework that unifies multilingual keyword semantics into shared atom ids, assigns docids to documents, and employs multi-step constrained decoding. a high-level overview of mgr-csc is shown in figure 1 (b). mgr-csc performs cross-lingual semantic compression by mapping semantically equivalent multilingual keywords to shared atom ids. for example, both the english word largest and its german counterpart größte are mapped to atom id 183. in addition, it applies dynamic decoding constraints to guide generation. specifically, mgr-csc contain three key parts. first, we extract explicit keywords from each multilingual document, and the document is represented by a set of multiple keywords. secondly, these keywords are projected into a shared latent space using unsupervised clustering. within this latent space, semantically equivalent expressions are assigned the same atom id, which effectively compresses the multilingual identifier space. finally, we introduce a multi-step dynamic constraint decoding strategy. the initial decoding step leverages the global frequency distribution of atom ids to guide selection. and a refinement step narrows the selection space based on constraints between atomic ids from preceding steps. comprehensive experiments on multiple benchmark datasets show that mgr-csc achieves outstanding performance, surpassing existing multilingual generative retrieval approaches by 6.83% on mmarco100k and 4.77% on mnq320k. furthermore, it substantially reduces the number of docid tokens by 74.51% on mmarco100k and 78.2% on mnq320k. the contributions of this paper are as follows: ## methodology in this section, we elaborate on our proposed method, multilingual generative retrieval via cross-lingual semantic compression, termed mgr-csc. the core idea is to assign an unique docid to each document by leveraging semantically similar key information across documents in different languages. this shared representation enables effective cross-lingual alignment and facilitates end-to-end semantic matching within a unified retrieval framework. as figure 2 illustrates, mgr-csc consists of three components: (1) extracting distinctive keywords from multilingual documents, (2) clustering multiple keywords into one semantic atom, and assigning each document a unique docid as a sequence of atoms, (3) during retrieval, the model generates the docid atom-by-atom under dynamic decoding constraints. ## conclusion this paper introduces mgr-csc, a multilingual generative retrieval method leveraging crosslingual semantic compression. this method employs semantic clustering to reduce multilingual docids and narrow the decoding space, and applies multi-step constrained decoding to restrict docid generation. the experimental reveals that our method consistently exhibit outstanding performance compared to existing retrieval approaches when applied to multilingual datasets.