View on GitHub


Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

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.


SentEval is an evaluation toolkit for evaluating sentence representations. It includes 17 downstream tasks, including common semantic textual similarity tasks. The semantic textual similarity (STS) benchmark tasks from 2012-2016 (STS12, STS13, STS14, STS15, STS16, STSB) measure the relatedness of two sentences based on the cosine similarity of the two representations. The evaluation criterion is Pearson correlation.

The SICK relatedness (SICK-R) task trains a linear model to output a score from 1 to 5 indicating the relatedness of two sentences. For the same dataset (SICK-E) can be treated as a binary classification problem using the entailment labels. The evaluation metric for SICK-R is Pearson correlation and classification accuracy for SICK-E.

The Microsoft Research Paraphrase Corpus (MRPC) corpus is a paraphrase identification dataset, where systems aim to identify if two sentences are paraphrases of each other. The evaluation metric is classification accuracy and F1.

The data can be downloaded from here.

Model MRPC SICK-R SICK-E STS Paper / Source  
GenSen (Subramanian et al., 2018) 78.6/84.4 0.888 87.8 78.9/78.6 Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning  
InferSent (Conneau et al., 2017) 76.2/83.1 0.884 86.3 75.8/75.5 Supervised Learning of Universal Sentence Representations from Natural Language Inference Data  

Paraphrase identification

Quora Question Pairs

The Quora Question Pairs dataset consists of over 400,000 pairs of questions on Quora. Systems must identify whether one question is a duplicate of the other. Models are evaluated based on accuracy.

Model Accuracy Paper / Source Code
DIIN (Gong et al., 2018) 89.06 Natural Language Inference Over Interaction Space Official
pt-DecAtt (Char) (Tomar et al., 2017) 88.40 Neural Paraphrase Identification of Questions with Noisy Pretraining  
BiMPM (Wang et al., 2017) 88.17 Bilateral Multi-Perspective Matching for Natural Language Sentences Official
GenSen (Subramanian et al., 2018) 87.01 Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning Official

Go back to the README