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Automatic Generation of Text Descriptive Comments for Code Blocks

AAAI Conferences

We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called CodeRNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.


China Lodges Stern Representations With U.S. Over Tiananmen Comments

U.S. News

Pompeo, speaking ahead of the 29-year anniversary of the day Chinese troops and tanks quashed the pro-democracy student-led demonstrations, called for Beijing to make a full accounting for those killed, detained or who went missing in the crackdown.


Automatic Generation of Text Descriptive Comments for Code Blocks

arXiv.org Artificial Intelligence

We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code-RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.


[D] HyperGAN with vector images • r/MachineLearning

@machinelearnbot

Hi, I was trying to google this but in my brief attempt could not find anything related to what I'm interested in. Most of the image generation methods I've seen look to be using pixels as i/o. Why is there not much focus on vector formats for images, such as svg or something? I understand you'd lose the CNN and the great work done there, but could vectorised images be much easier to train for some datasets, like digits or so on?


Lv

AAAI Conferences

Recent years have witnessed the boom of online sharing media contents, which raise significant challenges in effective management and retrieval. Though a large amount of efforts have been made, precise retrieval on video shots with certain topics has been largely ignored. At the same time, due to the popularity of novel time-sync comments, or so-called "bullet-screen comments", video semantics could be now combined with timestamps to support further research on temporal video labeling. In this paper, we propose a novel video understanding framework to assign temporal labels on highlighted video shots. To be specific, due to the informal expression of bullet-screen comments, we first propose a temporal deep structured semantic model (T-DSSM) to represent comments into semantic vectors by taking advantage of their temporal correlation. Then, video highlights are recognized and labeled via semantic vectors in a supervised way. Extensive experiments on a real-world dataset prove that our framework could effectively label video highlights with a significant margin compared with baselines, which clearly validates the potential of our framework on video understanding, as well as bullet-screen comments interpretation.