Next paper candidates: 11 Jan

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Next paper candidates

Let's propose papers to study next! All papers mentioned in the comments of this issue will be listed in the next vote.

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Last session runner-up: A general reinforcement learning algorithm that masters chess, shogi and Go through self-play

The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess) as well as Go.

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"Structured Neural Summarization" from ICLR'19

Keywords: Summarization, Graphs, Source Code Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

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"Code Attention: Translating Code to Comments by Exploiting Domain Features", 2017

Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension. However, due to the great cost of authoring with the comments, many code projects do not contain adequate comments. Automatic comment generation techniques have been proposed to generate comments from pieces of code in order to alleviate the human efforts in annotating the code. Most existing approaches attempt to exploit certain correlations (usually manually given) between code and generated comments, which could be easily violated if the coding patterns change and hence the performance of comment generation declines. In this paper, we first build C2CGit, a large dataset from open projects in GitHub, which is more than 20× larger than existing datasets. Then we propose a new attention module called Code Attention to translate code to comments, which is able to utilize the domain features of code snippets, such as symbols and identifiers. We make ablation studies to determine effects of different parts in Code Attention. Experimental results demonstrate that the proposed module has better performance over existing approaches in both BLEU and METEOR.

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"The Adverse Effects of Code Duplication in Machine Learning Models of Code", 2018

The field of big code relies on mining large corpora of code to perform some learning task. A significant threat to this approach has been recently identified by Lopes et al. (2017) who found a large amount of code duplication on GitHub. However, the impact of code duplication has not been noticed by researchers devising machine learning models for source code. In this article, we study the effect of code duplication to machine learning models showing that reported metrics are sometimes inflated by up to 100% when testing on duplicated code corpora compared to the performance on de-duplicated corpora which more accurately represent how machine learning models of code are used by software engineers. We present an "errata" for widely used datasets, list best practices for collecting code corpora and evaluating machine learning models on them, and release tools to help the community avoid this problem in future research.

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Hugo Mougard [10:33] Ok, results are out, we'll have a look at the AlphaZero paper :slightly_smiling_face:

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