Papers by Kevin Feng
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation (2022.coling-1)
Copied to clipboard
Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy
| Challenge: | Personifications are figures of speech that endow inanimate entities with properties and actions typically seen as requiring animacy. |
| Approach: | They propose to use personification data to train a parallel corpus of personifications . they propose to combine personification-related literalizations with automatic ones . |
| Outcome: | The proposed personification system can generate diverse and creative personifications . it can generate personification-related qualities such as interestingness and animacy . |
VIEWS: Entity-Aware News Video Captioning (2024.emnlp-main)
Copied to clipboard
Hammad Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Xuande Feng, Kevin Zhang, Jialu Liu, Shih-Fu Chang
| Challenge: | Existing video captioning benchmarks and models produce generic captions for videos that lack specific identification of individuals, locations, or organizations. |
| Approach: | They propose a task of directly summarizing news videos into captions that are entity-aware . they validate the effectiveness of their approach across three video captioning models . |
| Outcome: | The proposed approach is effective across three video captioning models. |
SPICA: Retrieving Scenarios for Pluralistic In-Context Alignment (2025.findings-acl)
Copied to clipboard
| Challenge: | In-context learning only considers similarity when drawing few-shot examples and not cross-group differences in values. |
| Approach: | They propose a framework that accounts for group-level differences during in-context example retrieval by using scenario banks, group-informed retrieval metrics, and in-constraint alignment prompts. |
| Outcome: | The proposed framework improves on an alignment task with groups seeing up to a +0.16 point improvement on a 5 point scale. |