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Using the pre-trained language models to understand source codes hasattracted increasing attention from financial institutions owing to the greatpotential to uncover financial risks. However, there are several challenges inapplying these language models to solve programming language-related problemsdirectly. For instance, the shift of domain knowledge between natural language(NL) and programming language (PL) requires understanding the semantic andsyntactic information from the data from different perspectives. To this end,we propose the AstBERT model, a pre-trained PL model aiming to betterunderstand the financial codes using the abstract syntax tree (AST).Specifically, we collect a sheer number of source codes (both Java and Python)from the Alipay code repository and incorporate both syntactic and semanticcode knowledge into our model through the help of code parsers, in which ASTinformation of the source codes can be interpreted and integrated. We evaluatethe performance of the proposed model on three tasks, including code questionanswering, code clone detection and code refinement. Experiment results showthat our AstBERT achieves promising performance on three different downstreamtasks.

AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees