In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed a significant growth. Recently, the incorporation of the copy mechanism with traditional encoder-decoder architectures and the use of pre-trained encoder-decoders have set new performance benchmarks. This paper presents a large variety of experiments that replicate and expand upon recent NMT-based SPARQL generation studies, comparing pre-trained and non-pre-trained models, question annotation formats, and the use of a copy mechanism for non-pre-trained and pre-trained models. Our results show that either adding the copy mechanism or using a question annotation improves performances for nonpre-trained models and for pre-trained models, setting new baselines for three popular datasets.
翻译：近年来，神经机器翻译（NMT）在SPARQL查询生成领域得到了显着增长。最近，将复制机制与传统的编码器-解码器架构相结合并使用预训练的编码器-解码器，已经设定了新的性能基准。本文介绍了广泛的实验，以复制和扩展最近的基于NMT的SPARQL生成研究，比较预训练和非预训练模型、问题注释格式以及非预训练和预训练模型的复制机制的使用。我们的结果表明，在非预训练模型和预训练模型中，添加复制机制或使用问题注释都可以提高性能，为三个流行数据集设定了新的基线。
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