Semantic Textual Similarity
564 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
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
UKPLab/sentence-transformers
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IJCNLP 2019
However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference computations (~65 hours) with BERT.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
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Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
huggingface/transformers
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arXiv 2019
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
google-research/ALBERT
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ICLR 2020
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
huggingface/transformers
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NeurIPS 2019
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.
Universal Sentence Encoder
facebookresearch/InferSent
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For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
XLNet: Generalized Autoregressive Pretraining for Language Understanding
zihangdai/xlnet
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NeurIPS 2019
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
SimCSE: Simple Contrastive Learning of Sentence Embeddings
princeton-nlp/SimCSE
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EMNLP 2021
This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
facebookresearch/InferSent
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EMNLP 2017
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
