96 citations · 105 across the 13 of their papers we have counts for
6 papers · 1 filter
Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling
Arie Cattan, Alon Eirew, Gabriel Stanovsky +2
Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation…
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics
Anthony Chen, Gabriel Stanovsky, Sameer Singh +1
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, pro…
Gender Coreference and Bias Evaluation at WMT 2020
Tom Kocmi, Tomasz Limisiewicz, Gabriel Stanovsky
Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as…
Active Learning for Coreference Resolution using Discrete Annotation
Belinda Z. Li, Gabriel Stanovsky, Luke Zettlemoyer
We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not co…
The Right Tool for the Job: Matching Model and Instance Complexities
Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta +2
As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference…
Ecological Semantics: Programming Environments for Situated Language Understanding
Ronen Tamari, Gabriel Stanovsky, Dafna Shahaf +1
Large-scale natural language understanding (NLU) systems have made impressive progress: they can be applied flexibly across a variety of tasks, and employ minimal structural assump…