Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
Table 2. Code Summarization Results of CO3 and Baselines
['Model', 'SQL BLEU4', 'SQL METEOR', 'Python BLEU4', 'Python METEOR']
To evaluate the performance of code summarization task, we use CODE-NN and a strong Seq2Seq model equipped with attention mechanism as baselines. CO3 is built upon the Seq2Seq model, and we find the introduction of code generation task and the adoption of dual learning and multi-task learning improve the performance by more than 15% in terms of BLEU4 and METEOR, which verifies the effectiveness of our approach. How each of these design choices contributes to the improvement will analysed in Sections 5.4 through 5.6.