Garbage In, Flowers Out: Noisy Training Data Help Generative Models at Test Time
Despite important progress, conversational systems often generate dialogues that sound unnatural to humans. We conjecture that the reason lies in the different training and testing conditions: agents are trained in a controlled “lab” setting but tested in the “wild”. During training, they learn to u...
Enregistré dans:
| Dans: | URI:https://journals.openedition.org/ijcol, |
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| Auteurs principaux: | , |
| Format: | Article ou chapitre numérique |
| Langue: | Anglais |
| Publié: |
IJCoL
2022
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| Accès en ligne: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |