Search Results for author: Luis Guzman-Nateras

Found 3 papers, 0 papers with code

Event Detection for Suicide Understanding

no code implementations Findings (NAACL) 2022 Luis Guzman-Nateras, Viet Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Nguyen

In particular, we introduce SuicideED: a new dataset for the ED task that features seven suicidal event types to comprehensively capture suicide actions and ideation, and general risk and protective factors.

Event Detection

Cross-Lingual Event Detection via Optimized Adversarial Training

no code implementations NAACL 2022 Luis Guzman-Nateras, Minh Van Nguyen, Thien Nguyen

In this work, we focus on Cross-Lingual Event Detection where a model is trained on data from a \textit{source} language but its performance is evaluated on data from a second, \textit{target}, language.

Event Detection

NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks

no code implementations ICLR 2020 Isaac Ahern, Adam Noack, Luis Guzman-Nateras, Dejing Dou, Boyang Li, Jun Huan

The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently.

Feature Importance

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