Paraphrase Identification On Quora Question

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
data2vec	92.4	data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Charformer-Tall	91.4	Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
RealFormer	91.34	RealFormer: Transformer Likes Residual Attention
StructBERTRoBERTa ensemble	90.7	StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
MFAE	90.54	What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis
XLNet-Large (ensemble)	90.3	XLNet: Generalized Autoregressive Pretraining for Language Understanding
Snorkel MeTaL(ensemble)	89.9	Training Complex Models with Multi-Task Weak Supervision
MT-DNN	89.6	Multi-Task Deep Neural Networks for Natural Language Understanding
SpanBERT	89.5	SpanBERT: Improving Pre-training by Representing and Predicting Spans
RE2	89.2	Simple and Effective Text Matching with Richer Alignment Features
MwAN	89.12	Multiway Attention Networks for Modeling Sentence Pairs
DIIN	89.06	Natural Language Inference over Interaction Space
MSEM	88.86	Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
Bi-CAS-LSTM	88.6	Cell-aware Stacked LSTMs for Modeling Sentences
pt-DecAtt	88.40	Neural Paraphrase Identification of Questions with Noisy Pretraining
TRANS-BLSTM	88.28	TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding
BiMPM	88.17	Bilateral Multi-Perspective Matching for Natural Language Sentences
GenSen	87.01	Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
1-3[0.8pt/2pt] Random	80	Self-Explaining Structures Improve NLP Models
FreeLB	74.8	SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

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Paraphrase Identification On Quora Question

Metrics

Results