Search Results for author: George Kour

Found 9 papers, 3 papers with code

Characterizing how 'distributional' NLP corpora distance metrics are

1 code implementation23 Oct 2023 Samuel Ackerman, George Kour, Eitan Farchi

We quantify this quality by constructing a Known-Similarity Corpora set from two paraphrase corpora and calculating the distance between paired corpora from it.

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

2 code implementations29 Nov 2022 George Kour, Samuel Ackerman, Orna Raz, Eitan Farchi, Boaz Carmeli, Ateret Anaby-Tavor

The ability to compare the semantic similarity between text corpora is important in a variety of natural language processing applications.

Semantic Similarity Semantic Textual Similarity

Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation

no code implementations22 Dec 2021 George Kour, Marcel Zalmanovici, Orna Raz, Samuel Ackerman, Ateret Anaby-Tavor

Testing Machine Learning (ML) models and AI-Infused Applications (AIIAs), or systems that contain ML models, is highly challenging.

Chatbot

Not Enough Data? Deep Learning to the Rescue!

1 code implementation8 Nov 2019 Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, Naama Zwerdling

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.

Data Augmentation General Classification +5

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

no code implementations ICLR 2019 Alon Jacovi, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Jonathan Berant

We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions.

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

no code implementations24 Apr 2018 Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Alon Jacovi

At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application.

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