INT : Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025)

Abstract: Multilingual spoken language understanding (SLU) involves intent detection (ID) and slot filling (SF) across multiple languages. The inherent linguistic diversity presents significant challenges in achieving performance comparable to traditional SLU. Recent studies have attempted to improve multilingual SLU performance by sharing multilingual encoders. However, these approaches have not directly established information flow between languages. To address this, we first demonstrate the feasibility of such information transfer and pinpoint the key challenges: prediction error mitigation and multilingual slot alignment. We then propose the INformation Transfer network (INT) to tackle these challenges. The gate unit in INT controls the information flow between languages, reducing the adverse impact of prediction errors on both ID and SF. Additionally, we reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages. Experimental results on the MASSIVE and MASSIVE-UG datasets show that our model outperforms all baselines in overall accuracy across all languages, and demonstrates robust performance when different languages are used as the source.

## introduction multilingual spoken language understanding (slu) shares the same two core tasks as traditional slu @xcite : intent detection (id) @xcite and slot filling (sf) @xcite . the key difference is that multilingual slu must process utterances in multiple languages, whereas traditional slu handles utterances in a single language. multilingual slu better aligns with real-world scenarios involving various languages @xcite , which has led to growing attention. traditional slu, particularly for high-resource languages like english @xcite or chinese @xcite , performs excellently with existing models @xcite . however, due to the inherent linguistic diversity of multilingual slu tasks, existing studies often struggle to achieve performance comparable to that on high-resource languages @xcite . one approach to improving multilingual slu performance is to enhance the capabilities of multilingual models themselves @xcite . these methods, though effective, often require a large amount of high-quality data, which is quite challenging. @xcite have sought to optimize multilingual representations for id and sf through data augmentation @xcite , using techniques like regularization @xcite and machine translation @xcite . while these techniques reduce reliance on the multilingual model's performance, they remain susceptible to the quality of translation apis. in contrast, @xcite ; @xcite capture the similarity between languages by sharing hidden states across multiple languages, without relying on external tools. these approaches, though promising, represent only an initial strategy. could directly establishing information transfer between languages further enhance the performance of multilingual slu? we conduct a preliminary experiment by taking the union of the output from each language with that of either a lower-performing or higherperforming language to explore the feasibility of information transfer. the results demonstrate that the overall accuracy of multilingual slu improves in both cases. this suggests that intent and slot information from the source language could potentially help to perform id and sf more accurately, making the establishment of information transfer feasible. however, establishing such a transfer may face the following challenges: we propose the information transfer network (int), which addresses the two challenges of establishing information transfer between languages in the following ways. for prediction error mitigation, we introduce a trainable gating unit during intent and slot information transfer, enabling the model to learn gate weights that control the flow of source language information. for multilingual slot alignment, we no longer treat sf as a sequence labeling task @xcite but as a span prediction task @xcite , and propose a slot-matching attention mechanism to facilitate information transfer between slots with equivalent meanings between different languages. experimental results show that the proposed int consistently outperforms baselines across multiple multilingual pre-trained models (mptms). notably, when different languages serve as the guiding source, our model retains a certain level of effectiveness. the contributions of this paper are as follows: ## method this section provides a detailed description of the proposed int, which facilitates the transfer of intent and slot information from the source language to multiple languages, thereby enhancing multilingual id and sf performance. as illustrated in figure 3 , our model consists of an encoding module ( § 3.1), an intent transfer module ( § 3.2), and a slot transfer module ( § 3.3). ## conclusion in this study, we investigate the feasibility of utilizing a source language to guide multiple languages in multilingual slu and identify the key challenges in establishing information transfer: prediction error mitigation and multilingual slot alignment. we propose int for transferring intent and slot information, where the gating unit effectively reduces the negative impact of source language prediction errors on multilingual slu performance. for multilingual slot alignment, we tackle the issue of inconsistent slot word counts through span prediction. we also introduce a slot-matching attention mechanism to address the issues of varying slot positions and sentence lengths across languages. experimental results on massive52 validate the effectiveness and robustness of our model.