Semantic Textual Similarity Edit Task
Natural Language Processing
About
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.
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Big Bird: Transformers for Longer Sequences
NeurIPS 2020
• tensorflow/models
• 
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Ranked #1 on
Text Classification
on arXiv
LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY TEXT CLASSIFICATION
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
ICLR 2020
• tensorflow/models
•
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Ranked #1 on
Natural Language Inference
on QNLI
LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SELF-SUPERVISED LEARNING SEMANTIC TEXTUAL SIMILARITY
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
ICLR 2021
• huggingface/transformers
• 
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Ranked #1 on
Question Answering
on SQuAD1.1 dev
COMMON SENSE REASONING COREFERENCE RESOLUTION LINGUISTIC ACCEPTABILITY NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
arXiv 2019
• huggingface/transformers
•
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).
Ranked #1 on
Natural Language Inference
on MultiNLI
ABSTRACTIVE TEXT SUMMARIZATION COMMON SENSE REASONING COREFERENCE RESOLUTION DOCUMENT SUMMARIZATION LINGUISTIC ACCEPTABILITY MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD SENSE DISAMBIGUATION
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
NeurIPS 2019
• huggingface/transformers
•
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.
Ranked #7 on
Semantic Textual Similarity
on STS Benchmark
KNOWLEDGE DISTILLATION LANGUAGE MODELLING LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TRANSFER LEARNING
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Ranked #2 on
Common Sense Reasoning
on SWAG
COMMON SENSE REASONING LANGUAGE MODELLING LEXICAL SIMPLIFICATION LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION SENTIMENT ANALYSIS
XLNet: Generalized Autoregressive Pretraining for Language Understanding
NeurIPS 2019
• huggingface/transformers
•
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
DOCUMENT RANKING HUMOR DETECTION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION QUESTION ANSWERING READING COMPREHENSION SENTIMENT ANALYSIS TEXT CLASSIFICATION
Improving Language Understanding by Generative Pre-Training
Preprint 2018
• huggingface/transformers
•
We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.
Ranked #3 on
Question Answering
on Story Cloze Test
DOCUMENT CLASSIFICATION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
IJCNLP 2019
• UKPLab/sentence-transformers
•
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.
Ranked #6 on
Semantic Textual Similarity
on STS Benchmark
(Spearman Correlation metric)
SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY SENTENCE EMBEDDINGS TRANSFER LEARNING
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
Ranked #1 on
Open-Domain Question Answering
on DuReader
CHINESE NAMED ENTITY RECOGNITION CHINESE READING COMPREHENSION CHINESE SENTENCE PAIR CLASSIFICATION CHINESE SENTIMENT ANALYSIS LINGUISTIC ACCEPTABILITY MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE OPEN-DOMAIN QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS