103 citations · 123 across the 6 of their papers we have counts for
9 papers
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…
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…
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
Peter Izsak, Shira Guskin, Moshe Wasserblat
Training models on low-resource named entity recognition tasks has been shown to be a challenge, especially in industrial applications where deploying updated models is a continuou…
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
Oren Pereg, Daniel Korat, Moshe Wasserblat +2
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction. The system is interpretable and user friendly and does not require labeled training da…