QQP
6 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in QQP
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GLUE
Most implemented papers
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge.
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks
Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e. g., $99. 99\%$ of examples are negatives).
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level
ruiqi-zhong/acl2021-instance-level
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Findings (ACL) 2021
We develop statistically rigorous methods to address this, and after accounting for pretraining and finetuning noise, we find that our BERT-Large is worse than BERT-Mini on at least 1-4% of instances across MNLI, SST-2, and QQP, compared to the overall accuracy improvement of 2-10%.
LEAP: Learnable Pruning for Transformer-based Models
Moreover, in order to reduce hyperparameter tuning, a novel adaptive regularization coefficient is deployed to control the regularization penalty adaptively.
Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation
lhryang/crl_egpg
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Findings (EMNLP) 2021
Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence.
Linear Connectivity Reveals Generalization Strategies
It is widely accepted in the mode connectivity literature that when two neural networks are trained similarly on the same data, they are connected by a path through parameter space over which test set accuracy is maintained.
