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
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor
Efficient Sentence Embedding using Discrete Cosine Transform
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents
Primer AI's Systems for Acronym Identification and Disambiguation
Contextualized Semantic Distance between Highly Overlapped Texts
Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Evaluation Discrepancy Discovery: A Sentence Compression Case-study
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment
Enhancing AMR-to-Text Generation with Dual Graph Representations
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Human-Robot Dialogue Annotation for Multi-Modal Common Ground
Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought
Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
Generating Text through Adversarial Training using Skip-Thought Vectors
Natural Language Decompositions of Implicit Content Enable Better Text Representations
A Survey on Sentence Embedding Models Performance for Patent Analysis
A Comparative Study on Structural and Semantic Properties of Sentence Embeddings
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings
Learning Dialogue Representations from Consecutive Utterances
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.