activity
20182022
most citedNot Enough Data? Deep Learning to the Rescue!

32 citations · 44 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2022

Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora

George Kour, Samuel Ackerman, Orna Raz +3

The ability to compare the semantic similarity between text corpora is important in a variety of natural language processing applications. However, standard methods for evaluating…

cs.HC20221 cited

High-quality Conversational Systems

Samuel Ackerman, Ateret Anaby-Tavor, Eitan Farchi +7

Conversational systems or chatbots are an example of AI-Infused Applications (AIIA). Chatbots are especially important as they are often the first interaction of clients with a bus…

cs.CL201932 cited

Not Enough Data? Deep Learning to the Rescue!

Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich +5

Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a…

cs.LG201911 cited

Neural network gradient-based learning of black-box function interfaces

Alon Jacovi, Guy Hadash, Einat Kermany +4

Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of e…

cs.LG2018

Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications

Guy Hadash, Einat Kermany, Boaz Carmeli +3

Existing applications include a huge amount of knowledge that is out of reach for deep neural networks. This paper presents a novel approach for integrating calls to existing appli…