Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation

• URL: http://arxiv.org/abs/2408.13432v1
• Date: Sat, 24 Aug 2024 01:58:28 GMT
• Title: Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation
• Authors: Yi-Hui Chen, Eric Jui-Lin Lu, Kwan-Ho Cheng,
• Abstract summary: This paper improves the ConvS2S encoder and adds multi-head attention from the Transformer, proposing a Multi-Head Conv encoder (MHC encoder) based on the n-gram language model. Ultimately, we found that the translation model based on the Multi-Head Conv encoder achieved better performance than other encoders.
• Score: 0.3413711585591077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The main task of the KGQA system (Knowledge Graph Question Answering) is to convert user input questions into query syntax (such as SPARQL). With the rise of modern popular encoders and decoders like Transformer and ConvS2S, many scholars have shifted the research direction of SPARQL generation to the Neural Machine Translation (NMT) architecture or the generative AI field of Text-to-SPARQL. In NMT-based QA systems, the system treats knowledge base query syntax as a language. It uses NMT-based translation models to translate natural language questions into query syntax. Scholars use popular architectures equipped with cross-attention, such as Transformer, ConvS2S, and BiLSTM, to train translation models for query syntax. To achieve better query results, this paper improved the ConvS2S encoder and added multi-head attention from the Transformer, proposing a Multi-Head Conv encoder (MHC encoder) based on the n-gram language model. The principle is to use convolutional layers to capture local hidden features in the input sequence with different receptive fields, using multi-head attention to calculate dependencies between them. Ultimately, we found that the translation model based on the Multi-Head Conv encoder achieved better performance than other encoders, obtaining 76.52\% and 83.37\% BLEU-1 (BiLingual Evaluation Understudy) on the QALD-9 and LC-QuAD-1.0 datasets, respectively. Additionally, in the end-to-end system experiments on the QALD-9 and LC-QuAD-1.0 datasets, we achieved leading results over other KGQA systems, with Macro F1-measures reaching 52\% and 66\%, respectively. Moreover, the experimental results show that with limited computational resources, if one possesses an excellent encoder-decoder architecture and cross-attention, experts and scholars can achieve outstanding performance equivalent to large pre-trained models using only general embeddings.

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