Search Results for author: Boaz Carmeli

Found 13 papers, 3 papers with code

Genie: Achieving Human Parity in Content-Grounded Datasets Generation

no code implementations25 Jan 2024 Asaf Yehudai, Boaz Carmeli, Yosi Mass, Ofir Arviv, Nathaniel Mills, Assaf Toledo, Eyal Shnarch, Leshem Choshen

Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization.

Long Form Question Answering

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

Emergent Quantized Communication

no code implementations4 Nov 2022 Boaz Carmeli, Ron Meir, Yonatan Belinkov

The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange.

Quantization

Exploration of the Usage of Color Terms by Color-blind Participants in Online Discussion Platforms

1 code implementation21 Oct 2022 Ella Rabinovich, Boaz Carmeli

Prominent questions about the role of sensory vs. linguistic input in the way we acquire and use language have been extensively studied in the psycholinguistic literature.

Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead

no code implementations24 Oct 2021 Eyal Ben-David, Boaz Carmeli, Ateret Anaby-Tavor

We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data.

counterfactual Goal-Oriented Dialogue Systems

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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