https://github.com/abaheti95/DC-NeuralConversation
공식 코드Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.
Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation
Semantic similarity prediction is better than other semantic similarity measures
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations
Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning
Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question
SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection
TLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification
CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs
Semantic Similarity Loss for Neural Source Code Summarization
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning
SemMT: A Semantic-based Testing Approach for Machine Translation Systems
Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation
One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling
Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models
Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval
Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild
Weak-shot Fine-grained Classification via Similarity Transfer
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.