464 citations · 628 across the 21 of their papers we have counts for
5 papers · 1 filter
Fast DistilBERT on CPUs
Haihao Shen, Ofir Zafrir, Bo Dong +7
Transformer-based language models have become the standard approach to solving natural language processing tasks. However, industry adoption usually requires the maximum throughput…
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
Phillip Howard, Arden Ma, Vasudev Lal +5
The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training an…
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM
Shira Guskin, Moshe Wasserblat, Chang Wang +1
Limited computational budgets often prevent transformers from being used in production and from having their high accuracy utilized. A knowledge distillation approach addresses the…
Efficient Few-Shot Learning Without Prompts
Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo +4
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, t…
TangoBERT: Reducing Inference Cost by using Cascaded Architecture
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +1
The remarkable success of large transformer-based models such as BERT, RoBERTa and XLNet in many NLP tasks comes with a large increase in monetary and environmental cost due to the…