STS
103 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in STS
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|
Libraries
Use these libraries to find STS models and implementations
MNIST
MTEB
ASSIN
Most implemented papers
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
caskcsg/sentemb
•
•
COLING 2022
Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair.
Smoothed Contrastive Learning for Unsupervised Sentence Embedding
caskcsg/sentemb
•
•
COLING 2022
Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding.
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
caskcsg/sentemb
•
•
Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer.
MTEB: Massive Text Embedding Benchmark
MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages.
AnglE-optimized Text Embeddings
SeanLee97/AnglE
•
•
This novel approach effectively mitigates the adverse effects of the saturation zone in the cosine function, which can impede gradient and hinder optimization processes.
Manipulating Large Language Models to Increase Product Visibility
We demonstrate that adding a strategic text sequence (STS) -- a carefully crafted message -- to a product's information page can significantly increase its likelihood of being listed as the LLM's top recommendation.
IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity.
CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity
SimengSun/CIS530-project
•
•
SEMEVAL 2017
We present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017.
Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i. e., the data usability is greatly reduced.
Learning Semantic Textual Similarity from Conversations
nickyeolk/info_retrieve
•
•
WS 2018
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data.
