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Register a new userHAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks
Haconvgnn: الانتباه الهرمي في الرسم البياني التضوير العلاجي في الشبكة العصبية لجيل الوثائق التعليمات البرمجية في أجهزة الكمبيوتر المحمولة Jupyter
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يسمح دفتر Jupyter لعلماء البيانات كتابة رمز تعلم الآلة مع وثائقها في الخلايا.في هذه الورقة، نقترح مهمة جديدة من توليد وثائق التعليمات البرمجية (CDG) لأجهزة الكمبيوتر المحمولة الحسابية.على النقيض من مهام CDG السابقة التي تركز على توليد وثائق لفظات شفرة واحدة، في دفتر ملاحظات حسابي، غالبا ما يتوافق وثائق في خلية في خلية تخطيطية مع خلايا التعليمات البرمجية المتعددة، ولديها خلايا التعليمات البرمجية هذه هيكل متأصل.اقترحنا نموذجا جديدا (Haconvgnn) الذي يستخدم آلية اهتمام هرمي للنظر في خلايا التعليمات البرمجية ذات الصلة ومعلومات الرموز التعليمية ذات الصلة عند إنشاء الوثائق.تم اختباره على كوربوس جديد تم إنشاؤه من أجهزة كمبيوتر دفاتر Kaggle موثقة جيدا، نظرا لأن نموذجنا يفوق النماذج الأساسية الأخرى.
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In contrast to the previous CDG tasks which focus on generating documentation for single code snippets, in a computational notebook, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. We proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation. Tested on a new corpus constructed from well-documented Kaggle notebooks, we show that our model outperforms other baseline models.
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