Comment Generation
19 papers with code • 0 benchmarks • 0 datasets
Article commenting poses new challenges for machines, as it involves multiple cognitive abilities: understanding the given article, formulating opinions and arguments, and organizing natu ral language for expression.
Benchmarks
These leaderboards are used to track progress in Comment Generation
Most implemented papers
InCoder: A Generative Model for Code Infilling and Synthesis
dpfried/incoder
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Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming.
Code Attention: Translating Code to Comments by Exploiting Domain Features
mf1832146/tree_transformer_2.0
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Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension.
Automating Code Review Activities by Large-Scale Pre-training
In this research, we focus on utilizing pre-training techniques for the tasks in the code review scenario.
CoNT: Contrastive Neural Text Generation
We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation.
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
lancopku/Graph-to-seq-comment-generation
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In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.
Automatic Generation of Personalized Comment Based on User Profile
Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation~(NLG) tasks.
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models
lethaiq/MALCOM
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In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem.
Retrieve and Refine: Exemplar-based Neural Comment Generation
Inspired by the IR-based and template-based approaches, in this paper, we propose a neural comment generation approach where we use the existing comments of similar code snippets as exemplars to guide comment generation.
Learning with Contrastive Examples for Data-to-Text Generation
aistairc/contrastive_data2text
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COLING 2020
Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e. g., {``}Nikkei gains{''} is generated when {``}Nikkei drops{''} is expected.
