310 citations · 582 across the 61 of their papers we have counts for
9 papers · 1 filter
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering
Dheeru Dua, Emma Strubell, Sameer Singh +1
Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these mo…
Error-aware Quantization through Noise Tempering
Zheng Wang, Juncheng B Li, Shuhui Qu +2
Quantization has become a predominant approach for model compression, enabling deployment of large models trained on GPUs onto smaller form-factor devices for inference. Quantizati…
DSI++: Updating Transformer Memory with New Documents
Sanket Vaibhav Mehta, Jai Gupta, Yi Tay +6
Differentiable Search Indices (DSIs) encode a corpus of documents in model parameters and use the same model to answer user queries directly. Despite the strong performance of DSI…
Bridging Fairness and Environmental Sustainability in Natural Language Processing
Marius Hessenthaler, Emma Strubell, Dirk Hovy +1
Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area…
SQuAT: Sharpness- and Quantization-Aware Training for BERT
Zheng Wang, Juncheng B Li, Shuhui Qu +2
Quantization is an effective technique to reduce memory footprint, inference latency, and power consumption of deep learning models. However, existing quantization methods suffer f…
A Survey of Active Learning for Natural Language Processing
Zhisong Zhang, Emma Strubell, Eduard Hovy
In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategie…
Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution
Nupoor Gandhi, Anjalie Field, Emma Strubell
Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, transferring these models to new target domains containing out-…
Measuring the Carbon Intensity of AI in Cloud Instances
Jesse Dodge, Taylor Prewitt, Remi Tachet Des Combes +7
By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which…
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
Clara Na, Sanket Vaibhav Mehta, Emma Strubell
Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern dee…