94 citations · 253 across the 41 of their papers we have counts for
8 papers · 1 filter
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Keshav Santhanam, Jon Saad-Falcon, Martin Franz +7
Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks. Unfortunately, some di…
SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformers
Ameet Deshpande, Md Arafat Sultan, Anthony Ferritto +3
Fine-tuning pre-trained language models (PLMs) achieves impressive performance on a range of downstream tasks, and their sizes have consequently been getting bigger. Since a differ…
Zero-Shot Dynamic Quantization for Transformer Inference
Yousef El-Kurdi, Jerry Quinn, Avirup Sil
We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing mod…
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
Scott McCarley, Mihaela Bornea, Sara Rosenthal +4
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We…
Task Transfer and Domain Adaptation for Zero-Shot Question Answering
Xiang Pan, Alex Sheng, David Shimshoni +3
Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning method…
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering
Md Arafat Sultan, Avirup Sil, Radu Florian
Machine learning models are prone to overfitting their training (source) domains, which is commonly believed to be the reason why they falter in novel target domains. Here we exami…