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.
State-of-the-art leaderboards
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Greatest papers with code
XLNet: Generalized Autoregressive Pretraining for Language Understanding
• huggingface/pytorch-pretrained-BERT
• 
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.
LANGUAGE MODELLING LINGUISTIC ACCEPTABILITY 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.
#2 best model for
Semantic Textual Similarity
on STS Benchmark
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
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 TEXTUAL SIMILARITY
A Hybrid Neural Network Model for Commonsense Reasoning
• namisan/mt-dnn
•
An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERT-based contextual encoder but use different model-specific input and output layers.
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
COLING 2018
• lanwuwei/SPM_toolkit
•
In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks.
NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTENCE PAIR MODELING
Character-based Neural Networks for Sentence Pair Modeling
NAACL 2018
• lanwuwei/SPM_toolkit
•
Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference.
NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY SENTENCE PAIR MODELING