Papers by Sedrick Scott Keh
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation (2022.coling-1)
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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 . |
PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically (2023.eacl-main)
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| Challenge: | Phonetic difficulty is hard to characterize and can be expressed in tongue twisters through alliteration and homophony. |
| Approach: | They propose a phoneme-aware neural completion to generate tongue twisters automatically . they leverage phoneme representations to capture phonetic difficulty and train language models . |
| Outcome: | The proposed language model generates novel, phonetically difficult, fluent, and semantically meaningful tongue twisters on two task settings. |