https://github.com/MUSE2022-HFUT/MuSe2022
공식 코드Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
Papers with Code 아카이브
Humor Detection
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Humor detection is the task of identifying comical or amusing elements.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Moradnejad/ColBERT-Using-BERT-Sentence-Embedding-for-Humor-Detection
공식 코드ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor
https://github.com/amazon-science/synthesizrr
공식 코드SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
https://github.com/amirip/muse-2024
공식 코드The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition
https://github.com/declare-lab/MISA
공식 코드MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
https://github.com/dmlc/xgboost
공식 코드https://github.com/eihw/muse2022
공식 코드The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
https://github.com/eihw/passau-sfch
공식 코드Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results
https://github.com/epfl-dlab/unfun
공식 코드https://github.com/lc0197/passau-sfch
공식 코드Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results
https://github.com/lwaekfjlk/mmoe
공식 코드MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts
https://github.com/malayyoub/emojis-to-train-emotion-classifiers
공식 코드https://github.com/orionw/RedditHumorDetection
공식 코드https://github.com/siddhantbikram/memeclip
공식 코드MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
https://github.com/yliu-cs/cvla
공식 코드https://github.com/zacharyhorvitz/getting-serious-with-llms
공식 코드Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models
https://github.com/zihangdai/xlnet
공식 코드XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/1082-datascience/finalproject-finalproject-1082ds_group3
https://github.com/2miatran/Natural-Language-Processing
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/360jinrong/GBST
https://github.com/Automunge/AutoMunge
https://github.com/Fickle519/stockforecastingmodelbasedon_XGboost
https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation
https://github.com/Javihaus/Auto-ML-app-with-dash
https://github.com/KPIxLILU/Machine-Learning-Workshop
https://github.com/MS-P3/code7/tree/main/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/MindCode-4/code-5/tree/main/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/MrCat9/Sklearn_Note
https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/SambhawDrag/XLNet.jl
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/agnesdeng/mixgb
https://github.com/bw2color/bw2color
https://github.com/chesterdu/contrastive_summary
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/cuhksz-nlp/SAPar
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/declare-lab/multimodal-deep-learning
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.