Semantic Textual Similarity
555 papers with code • 13 benchmarks • 17 datasets
Semantic textual similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.
Image source: Learning Semantic Textual Similarity from Conversations
Benchmarks
These leaderboards are used to track progress in Semantic Textual Similarity
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Libraries
Use these libraries to find Semantic Textual Similarity models and implementations
GLUE
MRPC
SICK
SentEval
CARER
MTEB
STS Benchmark
EVALution
CxC
Latest papers
Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers
dipamgoswami/fscil-calibration
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FSCIL methods start with a many-shot first task to learn a very good feature extractor and then move to the few-shot setting from the second task onwards.
Retrieval-Augmented Open-Vocabulary Object Detection
Specifically, RALF consists of two modules: Retrieval Augmented Losses (RAL) and Retrieval-Augmented visual Features (RAF).
SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination
Language models, particularly generative models, are susceptible to hallucinations, generating outputs that contradict factual knowledge or the source text.
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
microsoft/bimodalaudioseparation
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Conditional sound separation in multi-source audio mixtures without having access to single source sound data during training is a long standing challenge.
Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity
Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking.
SemEval Task 1: Semantic Textual Relatedness for African and Asian Languages
semantic-textual-relatedness/semantic_relatedness_semeval2024
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We present the first shared task on Semantic Textual Relatedness (STR).
Lightweight Embeddings for Graph Collaborative Filtering
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods.
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning
Previous work on multimodal sentence embedding has proposed multimodal contrastive learning and achieved promising results.
Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks
To rigorously evaluate the quality of our automatic labeling framework, we contribute an algorithm SIMILARITY to produce two novel metrics, temporal similarity and semantic similarity.
RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
Large Language Models (LLMs) increasingly support applications in a wide range of domains, some with potential high societal impact such as biomedicine, yet their reliability in realistic use cases is under-researched.
