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Comment Generation

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.

Papers

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Showing 1–50 of 65 papers

TitleStatusHype
InCoder: A Generative Model for Code Infilling and SynthesisCode2
Prompting for Numerical Sequences: A Case Study on Market Comment GenerationCode2
CoNT: Contrastive Neural Text GenerationCode2
Impact of Evaluation Methodologies on Code SummarizationCode1
Automating Code Review Activities by Large-Scale Pre-trainingCode1
Retrieve and Refine: Exemplar-based Neural Comment GenerationCode1
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations—0
APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation—0
Automatic Article Commenting: the Task and Dataset—0
Automatic Generation of News Comments Based on Gated Attention Neural Networks—0
Automating Horizon Scanning in Future Studies—0
MOPRD: A multidisciplinary open peer review dataset—0
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator—0
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP—0
Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation—0
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation—0
Read, Attend and Comment: A Deep Architecture for Automatic NewsComment Generation—0
ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews—0
Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?—0
Shared Task on Feedback Comment Generation for Language Learners—0
Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation—0
TAG : Type Auxiliary Guiding for Code Comment Generation—0
Topic Analysis for Text with Side Data—0
Toward a Task of Feedback Comment Generation for Writing Learning—0
Toward Imitating Visual Attention of Experts in Software Development Tasks—0
Towards Context-Aware Code Comment Generation—0
Towards Controlled and Diverse Generation of Article Comments—0
ViCo: Engaging Video Comment Generation with Human Preference Rewards—0
Creating Corpora for Research in Feedback Comment Generation—0
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn—0
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation—0
DiffuCom: A novel diffusion model for comment generation—0
DCA: Diversified Co-Attention towards Informative Live Video Commenting—0
Energy-bounded Learning for Robust Models of Code—0
Error syntax aware augmentation of feedback comment generation dataset—0
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation—0
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection—0
Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks—0
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition—0
Learning Comment Generation by Leveraging User-Generated Data—0
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels—0
Learning to Generate Code Comments from Class Hierarchies—0
Learning to Represent Programs with Heterogeneous Graphs—0
LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts—0
Live Video Comment Generation Based on Surrounding Frames and Live Comments—0
Market Comment Generation from Data with Noisy Alignments—0
Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System—0
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence ModelCode0
Code Attention: Translating Code to Comments by Exploiting Domain FeaturesCode0
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