Abstract
Pre-trained Relation Extraction (RE) models are widely employed in relation prediction in Knowledge Base Question Answering (KBQA). However, pre-trained models are usually optimized and evaluated on datasets (e.g. GLUE) which contain various Natural Language Processing (NLP) tasks except a RE task. As a result, it is difficult to select a best pre-trained model for relation prediction unless we evaluate all available pre-trained models on a relation prediction dataset. As the Semantic Similarity (SS) task in GLUE is similar to a RE task, a Semantic Similarity Model for Relation Prediction (SSMFRP) is proposed in this paper to convert a RE task in relation prediction to a SS task. In our model, a relation candidate in a RE model is converted into the corresponding question which contains a relation candidate. Then a modified SS model is employed to find the best-matched relation. Experimental results show that the effectiveness of our proposed model in relation prediction is related to the effectiveness of the original pre-trained model in SS and GLUE. Our model achieves an average accuracy of 91.2% with various pre-trained models and outperforms original models by an average margin of 1.8% with the similar training cost. In addition, further experiments show that our model is robust to abnormal input and outperforms original models by an average margin of 1.0% on datasets of abnormal input.
Supported by the Natural Science Foundation of China under Grant No. U21A20491, No. U1936109, No. U1908214.
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Wang, Z., Xu, X., Li, X., Song, X., Wei, X., Huang, D. (2022). SSMFRP: Semantic Similarity Model for Relation Prediction in KBQA Based on Pre-trained Models. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_25
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