1 code implementation • 14 Feb 2023 • Mahnaz Koupaee, Greg Durrett, Nathanael Chambers, Niranjan Balasubramanian
Event scenarios are often complex and involve multiple event sequences connected through different entity participants.
no code implementations • 31 Jul 2022 • Sayontan Ghosh, Mahnaz Koupaee, Isabella Chen, Francis Ferraro, Nathanael Chambers, Niranjan Balasubramanian
This dataset contains inferable participant states; a counterfactual perturbation to each state; and the changes to the story that would be necessary if the counterfactual were true.
no code implementations • ACL 2021 • Mahnaz Koupaee, Greg Durrett, Nathanael Chambers, Niranjan Balasubramanian
Event language models represent plausible sequences of events.
1 code implementation • COLING 2020 • Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, Niranjan Balasubramanian
We introduce PerSenT, a dataset of crowd-sourced annotations of the sentiment expressed by the authors towards the main entities in news articles.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Heeyoung Kwon, Mahnaz Koupaee, Pratyush Singh, Gargi Sawhney, Anmol Shukla, Keerthi Kumar Kallur, Nathanael Chambers, Niranjan Balasubramanian
This paper introduces PeKo, a crowd-sourced annotation of preconditions between event pairs in newswire, an order of magnitude larger than prior text annotations.
9 code implementations • 18 Oct 2018 • Mahnaz Koupaee, William Yang Wang
Sequence-to-sequence models have recently gained the state of the art performance in summarization.
Ranked #3 on Text Summarization on WikiHow
no code implementations • 18 Oct 2018 • Mahnaz Koupaee, William Yang Wang
Convolutional neural networks have been successfully applied to various NLP tasks.