30 citations · 47 across the 7 of their papers we have counts for
7 papers
Zonkey: A Hierarchical Diffusion Language Model with Differentiable Tokenization and Probabilistic Attention
Alon Rozental
Large language models (LLMs) have revolutionized natural language processing, yet they remain constrained by fixed, non-differentiable tokenizers like Byte Pair Encoding (BPE), whi…
Adversarial Generation and Encoding of Nested Texts
Alon Rozental
In this paper we propose a new language model called AGENT, which stands for Adversarial Generation and Encoding of Nested Texts. AGENT is designed for encoding, generating and ref…
Latent Universal Task-Specific BERT
Alon Rozental, Zohar Kelrich, Daniel Fleischer
This paper describes a language representation model which combines the Bidirectional Encoder Representations from Transformers (BERT) learning mechanism described in Devlin et al.…
Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding
Alon Rozental, Dadi Biton
This article describes Amobee's participation in "HatEval: Multilingual detection of hate speech against immigrants and women in Twitter" (task 5) and "OffensEval: Identifying and…
Amobee at IEST 2018: Transfer Learning from Language Models
Alon Rozental, Daniel Fleischer, Zohar Kelrich
This paper describes the system developed at Amobee for the WASSA 2018 implicit emotions shared task (IEST). The goal of this task was to predict the emotion expressed by missing w…
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification
Alon Rozental, Daniel Fleischer
This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks.…
Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter
Alon Rozental, Daniel Fleischer
This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on…