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Found 15 papers, 10 papers with code
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models
Motivated by such considerations, we propose a collaborative optimization for PLMs that integrates static model compression and dynamic inference acceleration.
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
To facilitate the application of VLP to VQA tasks, it is imperative to jointly study VLP compression and OOD robustness, which, however, has not yet been explored.
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training
Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation
In this paper, however, we observe that although distilling the teacher's hidden state knowledge (HSK) is helpful, the performance gain (marginal utility) diminishes quickly as more HSK is distilled.
ROSITA: Refined BERT cOmpreSsion with InTegrAted techniques
Based on the empirical findings, our best compressed model, dubbed Refined BERT cOmpreSsion with InTegrAted techniques (ROSITA), is $7. 5 \times$ smaller than BERT while maintains $98. 5\%$ of the performance on five tasks of the GLUE benchmark, outperforming the previous BERT compression methods with similar parameter budget.
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage.
Exploring and Distilling Cross-Modal Information for Image Captioning
Recently, attention-based encoder-decoder models have been used extensively in image captioning.
Unsupervised Pre-training for Natural Language Generation: A Literature Review
They are classified into architecture-based methods and strategy-based methods, based on their way of handling the above obstacle.
Ranking and Sampling in Open-Domain Question Answering
Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs.
Self-Adaptive Scaling for Learnable Residual Structure
Residual has been widely applied to build deep neural networks with enhanced feature propagation and improved accuracy.
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance.
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
The encode-decoder framework has shown recent success in image captioning.
