Papers with Code 아카이브

Comment Generation

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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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Gompyn/re2com-opensource

공식 코드

Retrieve and Refine: Exemplar-based Neural Comment Generation

https://github.com/NecoraNyaru/PInG

공식 코드

Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding

https://github.com/Walleclipse/AGPC

공식 코드

Automatic Generation of Personalized Comment Based on User Profile

https://github.com/aim3-ruc/movielc

공식 코드

Knowledge Enhanced Model for Live Video Comment Generation

https://github.com/aistairc/market-reporter

공식 코드

Prompting for Numerical Sequences: A Case Study on Market Comment Generation

https://github.com/ase-decom/ase22_decom

공식 코드

Automatic Comment Generation via Multi-Pass Deliberation

https://github.com/dpfried/incoder

공식 코드

InCoder: A Generative Model for Code Infilling and Synthesis

https://github.com/engineeringsoftware/time-segmented-evaluation

공식 코드

Impact of Evaluation Methodologies on Code Summarization

https://github.com/google-research-datasets/ToTTo

공식 코드

CoNT: Contrastive Neural Text Generation

https://github.com/lancopku/Graph-to-seq-comment-generation

공식 코드

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

https://github.com/lethaiq/MALCOM

공식 코드

MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models

https://github.com/mesia-codecomment/mesia

공식 코드

MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation

https://github.com/microsoft/CodeBERT

공식 코드

Automating Code Review Activities by Large-Scale Pre-training

https://github.com/mkhfring/largelanguagemodels

공식 코드

Investigating the Efficacy of Large Language Models for Code Clone Detection

https://github.com/oussamasghaier/rl4cr

공식 코드

Leveraging Reward Models for Guiding Code Review Comment Generation

https://github.com/sairin1202/storyer

공식 코드

StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning

https://github.com/shark-nlp/cont

공식 코드

CoNT: Contrastive Neural Text Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.