https://github.com/Aichiniuroumian/RCLMuFN
공식 코드RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection
Papers with Code 아카이브
Sarcasm Detection
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The goal of Sarcasm Detection is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Akshitaag/Sarcasm_Detection
공식 코드https://github.com/CoderChen01/InterCLIP-MEP
공식 코드InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection
https://github.com/GalaxyGHz/Diploma
공식 코드https://github.com/LCS2-IIITD/Hyphen
공식 코드https://github.com/LCS2-IIITD/MOSES
공식 코드Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues
https://github.com/LCS2-IIITD/MSH-COMICS
공식 코드Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations
https://github.com/abdelkadermh/isarcasmeval
공식 코드https://github.com/amirabaskohi/semeval2022-task6-sarcasm-detection
공식 코드https://github.com/apoorva-nunna/mustard_plus_plus
공식 코드https://github.com/bfelbo/deepmoji
공식 코드https://github.com/bitbitlemon/CGL-MHA
공식 코드An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework
https://github.com/brcsomnath/commonsense-sarcasm
공식 코드https://github.com/bshmueli/SPIRS
공식 코드Reactive Supervision: A New Method for Collecting Sarcasm Data
https://github.com/caisa-lab/sarcasm_detection
공식 코드Perceived and Intended Sarcasm Detection with Graph Attention Networks
https://github.com/changke123/clfa
공식 코드Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment
https://github.com/copsyn/csc
공식 코드Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!
https://github.com/cpaaax/multimodal_cast
공식 코드Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification
https://github.com/danaesavi/socialmedia-textimage-classification-auxlosses
공식 코드Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks
https://github.com/debanjanghosh/sarcasm_context
공식 코드The Role of Conversation Context for Sarcasm Detection in Online Interactions
https://github.com/fesvhtr/DocMSU
공식 코드DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding
https://github.com/guoxuxu/LOANT
공식 코드Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection
https://github.com/joeying1019/mmsd2.0
공식 코드MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
https://github.com/kolchinski/reddit-sarc
공식 코드https://github.com/lbotirx/cofipara
공식 코드https://github.com/lcs2-iiitd/maf
공식 코드When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues
https://github.com/less-and-less-bugs/hkemodel
공식 코드Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement
https://github.com/likemycode/codemix
공식 코드How Effective is Incongruity? Implications for Code-mix Sarcasm Detection
https://github.com/lwaekfjlk/mmoe
공식 코드MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts
https://github.com/lwsam/ASAP-Gaze
공식 코드https://github.com/mattiadg/Sarcasm-LSA
공식 코드https://github.com/mostafa-mahmoud/chatgpt-wide-evaluation
공식 코드https://github.com/passare-ru/passarefunctions
공식 코드https://github.com/perathambkk/euphemism_shared_task_emnlp2022
공식 코드TEDB System Description to a Shared Task on Euphemism Detection 2022
https://github.com/priyank96/dataset-pruning-sarcasm-detection
공식 코드https://github.com/rishabhmisra/Sarcasm-Detection-using-CNN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.