4 citations · 7 across the 2 of their papers we have counts for
6 papers
Toward Diverse Precondition Generation
Heeyoung Kwon, Nathanael Chambers, Niranjan Balasubramanian
Language understanding must identify the logical connections between events in a discourse, but core events are often unstated due to their commonsense nature. This paper fills in…
Modeling Preconditions in Text with a Crowd-sourced Dataset
Heeyoung Kwon, Mahnaz Koupaee, Pratyush Singh +5
Preconditions provide a form of logical connection between events that explains why some events occur together and information that is complementary to the more widely studied rela…
Modeling Label Semantics for Predicting Emotional Reactions
Radhika Gaonkar, Heeyoung Kwon, Mohaddeseh Bastan +2
Predicting how events induce emotions in the characters of a story is typically seen as a standard multi-label classification task, which usually treats labels as anonymous classes…
Generating Narrative Text in a Switching Dynamical System
Noah Weber, Leena Shekhar, Heeyoung Kwon +2
Early work on narrative modeling used explicit plans and goals to generate stories, but the language generation itself was restricted and inflexible. Modern methods use language mo…
Repurposing Entailment for Multi-Hop Question Answering Tasks
Harsh Trivedi, Heeyoung Kwon, Tushar Khot +2
Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which req…
Fake Sentence Detection as a Training Task for Sentence Encoding
Viresh Ranjan, Heeyoung Kwon, Niranjan Balasubramanian +1
Sentence encoders are typically trained on language modeling tasks with large unlabeled datasets. While these encoders achieve state-of-the-art results on many sentence-level tasks…