Popular GitHub Repositories for Triple Wins

[ACL 2020] A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

Unsupervised Speech Decomposition Via Triple Information Bottleneck

[WWWJ 2024] LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities

Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

基于pytorch的中文三元组提取（命名实体识别+关系抽取）

[EMNLP 2020] OpenUE: An Open Toolkit of Universal Extraction from Text

The official repo of One RL to See Them All: Visual Triple Unified Reinforcement Learning

See Triple-GAN-V2 in PyTorch: https://github.com/taufikxu/Triple-GAN

GANnotation (PyTorch): Landmark-guided face to face synthesis using GANs (And a triple consistency loss!)

CoDEx: A set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia

Triple Branch BERT Siamese Network for fake news classification on LIAR-PLUS dataset in PyTorch

TriAAN-VC: Triple Adaptive Attention Normalization for Any-to-Any Voice Conversion

[CVPR 2022] "Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations" by Tianlong Chen*, Peihao Wang*, Zhiwen Fan, Zhangyang Wang

[EMNLP 2022] Generative Knowledge Graph Construction: A Review

Multimodal entity linking (MEL) aims to utilize multimodal information to map mentions to corresponding entities defined in knowledge bases. We release three MEL datasets: Weibo-MEL, Wikidata-MEL and Richpedia-MEL, containing 25,602, 18,880 and 17,806 samples from social media, encyclopedia and multimodal knowledge graphs respectively. A MEL dataset construction approach is proposed, including five stages: multimodal information extraction, mention extraction, entity extraction, triple construction and dataset construction. Experiment results demonstrate the usability of the datasets and the distinguishability between baseline models.

Transformer-based Memory Networks for Knowledge Graph Embeddings (ACL 2020) (Pytorch and Tensorflow)

Agentic Ontology Assisted Framework for Semantic Triple Extraction

[SIGIR 2023] Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction

[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“

[EMNLP 2023 (Findings)] Schema-adaptable Knowledge Graph Construction