Dan Goldwasser

Introducing DRAIL – a Step Towards Declarative Deep Relational Learning

2016

We introduce DRAIL, a new declarative framework for specifying Deep Relational Models. Our framework separates structural considerations, which express domain knowledge, from the learning architecture to simplify the process of building complex structural models.

Modeling Content and Context with Deep Relational Learning

2021 · Transactions of the Association for Computational Linguistics

Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the …

Towards Few-Shot Identification of Morality Frames using In-Context Learning

2022

Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot identification of these concepts is desirable. Few-shot in-context learning using …

Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries

2023

Understanding and characterizing the discus- sions around key events in news streams is important for analyzing political discourse. In this work, we study the problem of identification of such key events and the news articles …

"Hiding in Plain Sight": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms

2024 · arXiv (Cornell University)

Naturally situated conversations encapsulate the social norms inherent to their context, reflecting both the relationships between interlocutors and the underlying communicative intent. In this paper, we propose a novel, multi-step framework for generating dialogues that …