Papers by Heeyoung Kwon
Toward Diverse Precondition Generation (2021.starsem-1)
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| Challenge: | a typical goal for language understanding is to logically connect the events of a discourse, but connective events are not described due to their commonsense nature. |
| Approach: | They propose a system that generates unique and diverse preconditions by using an event sampler, candidate generator, and post-processor. |
| Outcome: | The proposed system can generate unique and diverse preconditions without training on diverse examples. |
Modeling Label Semantics for Predicting Emotional Reactions (2020.acl-main)
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| Challenge: | Existing methods for predicting how events induce emotions ignore the semantics of the labels themselves. |
| Approach: | They propose that the semantics of emotion labels can guide a model’s attention when representing the input story. |
| Outcome: | The proposed model can model the semantics of emotion labels and track correlations on unlabeled data. |
Modeling Preconditions in Text with a Crowd-sourced Dataset (2020.findings-emnlp)
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Heeyoung Kwon, Mahnaz Koupaee, Pratyush Singh, Gargi Sawhney, Anmol Shukla, Keerthi Kumar Kallur, Nathanael Chambers, Niranjan Balasubramanian
| Challenge: | Existing methods for modeling preconditions in text are limited due to the lack of large scale labeled data grounded in text. |
| Approach: | They propose a crowd-sourced annotation of preconditions between event pairs in newswire that is larger than prior annotations. |
| Outcome: | The proposed model outperforms existing models on two task sets, showing that precondition knowledge is not easily accessible in LM-derived representations alone. |
Repurposing Entailment for Multi-Hop Question Answering Tasks (N19-1)
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| Challenge: | Existing approaches to use entailment models for question answering are limited . large scale datasets are typically framed at a sentence level, whereas question answering requires verifying whether multiple sentences, taken together as a premise, entitle a hypothesis. |
| Approach: | They propose a general architecture that can use entailment models for multi-hop QA tasks. |
| Outcome: | The proposed model outperforms QA models trained on target datasets and the OpenAI transformer models. |