Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites

Posts in software Q\&A sites often consist of three main parts: title, description and code, which are interconnected and jointly describe the question. Existing tag recommendation methods often treat different modalities as a whole or inadequately consider the interaction between different modalities. Additionally, they focus on extracting information directly from the post itself, neglecting the information from external knowledge sources. Therefore, we propose a Retrieval Augmented Cross-Modal (RACM) Tag Recommendation Model in Software Q\&A Sites. Specifically, we first use the input post as a query and enhance the representation of different modalities by retrieving information from external knowledge sources. For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code. In the fusion process, a gate mechanism is employed to achieve fine-grained feature selection, controlling the amount of information extracted from the submodalities. Finally, the fused information is used for tag recommendation. Experimental results on three real-world datasets demonstrate that our model outperforms the state-of-the-art counterparts.