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Code generation focuses on the automatic conversion of natural language (NL)utterances into code snippets. The sequence-to-tree (Seq2Tree) approaches areproposed for code generation, with the guarantee of the grammatical correctnessof the generated code, which generate the subsequent Abstract Syntax Tree (AST)node relying on antecedent predictions of AST nodes. Existing Seq2Tree methodstend to treat both antecedent predictions and subsequent predictions equally.However, under the AST constraints, it is difficult for Seq2Tree models toproduce the correct subsequent prediction based on incorrect antecedentpredictions. Thus, antecedent predictions ought to receive more attention thansubsequent predictions. To this end, in this paper, we propose an effectivemethod, named Antecedent Prioritized (AP) Loss, that helps the model attachimportance to antecedent predictions by exploiting the position information ofthe generated AST nodes. We design an AST-to-Vector (AST2Vec) method, that mapsAST node positions to two-dimensional vectors, to model the positioninformation of AST nodes. To evaluate the effectiveness of our proposed loss,we implement and train an Antecedent Prioritized Tree-based code generationmodel called APT. With better antecedent predictions and accompanyingsubsequent predictions, APT significantly improves the performance. We conductextensive experiments on four benchmark datasets, and the experimental resultsdemonstrate the superiority and generality of our proposed method.

Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation