SPBERT: Pre-training BERT on SPARQL Queries for End-to-end Question Answering over Knowledge Graphs

• URL: http://arxiv.org/abs/2106.09997v1
• Date: Fri, 18 Jun 2021 08:39:26 GMT
• Title: SPBERT: Pre-training BERT on SPARQL Queries for End-to-end Question Answering over Knowledge Graphs
• Authors: Hieu Tran, Long Phan, and Truong-Son Nguyen
• Abstract summary: SPBERT is a Transformer-based language model pre-trained on massive SPARQL query logs. We investigate how SPBERT and encoder-decoder architecture can be adapted for Knowledge-based QA corpora.
• Score: 1.1775939485654976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We aim to create an unprecedented attempt to build an end-to-end Question Answering (QA) over Knowledge Graphs (KGs), which can construct SPARQL queries from natural language questions and generate a verbalized answer to its queries. Hence, we introduce SPBERT, a Transformer-based language model pre-trained on massive SPARQL query logs. By incorporating masked language modelling objective and word structural objective, SPBERT can learn general-purpose representations in both natural language and SPARQL query language and make the most of the sequential order of words that are crucial for structured language like SPARQL. In this paper, we investigate how SPBERT and encoder-decoder architecture can be adapted for Knowledge-based QA corpora. We conduct exhaustive experiments on two auxiliary tasks, including SPARQL Query Construction and Answer Verbalization Generation. Results show that SPBERT obtains promising performance and achieves state-of-the-art results on several of these tasks.

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