Papers by Guy Rotman
Designing an Automatic Agent for Repeated Language–based Persuasion Games (2022.tacl-1)
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| Challenge: | Existing work on persuasion games assumes communication with stylized messages that do not consist of real-world natural language. |
| Approach: | They propose to use a repeated sender-decision maker game to persuade a receiver to accept a deal by sending one of several possible natural language reviews to the expert. |
| Outcome: | The proposed expert is superior to baselines and adaptable to different decision makers and potential proposed deals. |
Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations (2026.findings-acl)
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| Challenge: | In-context learning (ICL) is the standard method for low-resource classification, yet its efficacy in specialized domains remains largely unexplored. |
| Approach: | They propose a framework that distills verbose examples into compact, interpretable representations of structured classification criteria and precise task descriptions. |
| Outcome: | The proposed method achieves 99% reduction in token usage and improves macro-averaged AUC by up to 7% over traditional ICL. |
Multi-task Active Learning for Pre-trained Transformer-based Models (2022.tacl-1)
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| Challenge: | Multi-task learning requires annotating the same text with multiple annotation schemes, which can be costly and laborious. |
| Approach: | They propose to use multi-task active learning paradigm to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. |
| Outcome: | The proposed model minimizes annotation efforts for multi-task NLP models by iterating on the most valuable examples. |
Bridging Languages through Images with Deep Partial Canonical Correlation Analysis (P18-1)
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| Challenge: | a deep neural network can be used to improve bilingual text embeddings . a novel approach is proposed to optimize text embed-ings on shared visual information . |
| Approach: | They propose a deep neural network that leverages images to improve bilingual text embeddings. |
| Outcome: | The proposed model outperforms previous methods on word similarity and cross-lingual image description retrieval. |
Model Compression for Domain Adaptation through Causal Effect Estimation (2021.tacl-1)
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| Challenge: | Existing methods for compressing language representation models are not interpretable and do not consider the differences in the predictive power of various model components or the generalizability of the compressed models. |
| Approach: | They propose a model compression scheme that estimates the average treatment effect of a single layer on the model's predictions. |
| Outcome: | The proposed model compression scheme outperforms strong baselines on dozens of domain pairs across three text classification and sequence tagging tasks. |