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Semantic Similarity

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하위 태스크: 2
평가표: 28
보존된 평가 기록: 21

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/sharif-slpl/se-2024-task-06-shroom

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SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination

https://github.com/shenzheng2000/tpsence

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TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain

https://github.com/showlab/diffsim

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DiffSim: Taming Diffusion Models for Evaluating Visual Similarity

https://github.com/shubha07m/Energy-optimized-semantic-loss

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Transformers for Green Semantic Communication: Less Energy, More Semantics

https://github.com/shumingma/SRB

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A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification

https://github.com/sicara/semantic-task-sampling

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Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling

https://github.com/smartandcleverrobot/icra-cirn

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Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network

https://github.com/sohomghosh/FinSim_Financial_Hypernym_detection

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Term Expansion and FinBERT fine-tuning for Hypernym and Synonym Ranking of Financial Terms

https://github.com/solar-group/agileeffortestimation

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Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study

https://github.com/sssadrizadeh/transfool

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TransFool: An Adversarial Attack against Neural Machine Translation Models

https://github.com/stanfordnlp/treelstm

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Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

https://github.com/steffeneger/global-explainability-metrics

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Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors

https://github.com/steventan0110/NMTModelAttack

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Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation

https://github.com/sunisfighting/signa

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Single-View Graph Contrastive Learning with Soft Neighborhood Awareness

https://github.com/swaroop-nath/rl-qfs

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Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning

https://github.com/sydney-machine-learning/sentimentanalysis_bhagavadgita

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Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework

https://github.com/tawald/semantic_rsm

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Decoupling Semantic Similarity from Spatial Alignment for Neural Networks

https://github.com/tbmihailov/semeval2016-task3-CQA

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SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings

https://github.com/tca19/near-lossless-binarization

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Near-lossless Binarization of Word Embeddings

https://github.com/thompsonb/prism

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Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity

https://github.com/thu-coai/HINT

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Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence

https://github.com/thuiar/textoir

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A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery

https://github.com/tianyu139/meaning-as-trajectories

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Meaning Representations from Trajectories in Autoregressive Models

https://github.com/timpal0l/sts-benchmark-swedish

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Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity

https://github.com/timrudner/function-space-empirical-bayes

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Function-Space Regularization in Neural Networks: A Probabilistic Perspective

https://github.com/tjzvbokbnft/ELITE-Embedding-Less-retrieval-with-Iterative-Text-Exploration

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ELITE: Embedding-Less retrieval with Iterative Text Exploration

https://github.com/ttuhamg/driveditfit

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DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving

https://github.com/tumeteor/ttp-mapping

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Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition

https://github.com/ubc-nlp/turjuman

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TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation

https://github.com/uclanlp/ParaBART

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Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models

https://github.com/uclanlp/paraamr

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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation

https://github.com/uhh-lt/path2vec

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Making Fast Graph-based Algorithms with Graph Metric Embeddings

https://github.com/umdatasciencelab/uncertainty-quantification-for-llms

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Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models

https://github.com/usc-tamagotchi/dh-mlm

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The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining

https://github.com/vaishwarya96/hierarchy-image-retrieval

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Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval

https://github.com/vboussot/impactloss

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IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration

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