Semantic Textual Similarity
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.
Leaderboards
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Greatest papers with code
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.
#5 best model for
Semantic Textual Similarity
on MRPC
LANGUAGE MODELLING LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TRANSFER LEARNING
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
SOTA for Natural Language Inference on QNLI
LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY
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 LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TEXT CLASSIFICATION
RoBERTa: A Robustly Optimized BERT Pretraining Approach
• huggingface/pytorch-transformers
•
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
SOTA for Question Answering on SQuAD2.0 dev (using extra training data)
LANGUAGE MODELLING LEXICAL SIMPLIFICATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS
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.
#5 best model for
Question Answering
on Quora Question Pairs
LINGUISTIC ACCEPTABILITY MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
ICLR 2018
• facebookresearch/InferSent
•
In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.
MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY
Universal Sentence Encoder
• facebookresearch/InferSent
•
For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
SOTA for Text Classification on TREC-6
SEMANTIC TEXTUAL SIMILARITY SENTENCE EMBEDDINGS SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD EMBEDDINGS
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
EMNLP 2017
• facebookresearch/InferSent
•
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
CROSS-LINGUAL NATURAL LANGUAGE INFERENCE SEMANTIC TEXTUAL SIMILARITY TRANSFER LEARNING WORD EMBEDDINGS
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• google-research/text-to-text-transfer-transformer
• 
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).
SOTA for Linguistic Acceptability on CoLA
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
Improving Language Understanding by Generative Pre-Training
Preprint 2018
• openai/finetune-transformer-lm
•
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.
#4 best model for
Natural Language Inference
on SNLI
DOCUMENT CLASSIFICATION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY