2 citations · 3 across the 6 of their papers we have counts for
6 papers
Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations
Guy Rotman, Adi Kopilov, Danit Berger Zalmanson +1
In-context learning (ICL) is the standard method for low-resource classification, yet its efficacy in specialized domains remains largely unexplored. We address the challenge of cl…
Multi-task Active Learning for Pre-trained Transformer-based Models
Guy Rotman, Roi Reichart
Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predic…
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
Alon Berliner, Guy Rotman, Yossi Adi +2
Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of hig…
Designing an Automatic Agent for Repeated Language based Persuasion Games
Maya Raifer, Guy Rotman, Reut Apel +2
Persuasion games are fundamental in economics and AI research and serve as the basis for important applications. However, work on this setup assumes communication with stylized mes…
Model Compression for Domain Adaptation through Causal Effect Estimation
Guy Rotman, Amir Feder, Roi Reichart
Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led t…
Deep Contextualized Self-training for Low Resource Dependency Parsing
Guy Rotman, Roi Reichart
Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data,…