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- W4287670057 abstract "Natural language generation provides designers with methods for automatically generating text, e.g. for creating summaries, chatbots and game content. In practise, text generators are often either learned and hard to interpret, or created by hand using techniques such as grammars and templates. In this paper, we introduce a novel grammar induction algorithm for learning interpretable grammars for generative purposes, called Gitta. We also introduce the novel notion of template trees to discover latent templates in corpora to derive these generative grammars. By using existing human-created grammars, we found that the algorithm can reasonably approximate these grammars using only a few examples. These results indicate that Gitta could be used to automatically learn interpretable and easily modifiable grammars, and thus provide a stepping stone for human-machine co-creation of generative models." @default.
- W4287670057 created "2022-07-25" @default.
- W4287670057 creator A5003495739 @default.
- W4287670057 creator A5078136609 @default.
- W4287670057 date "2020-09-09" @default.
- W4287670057 modified "2023-09-26" @default.
- W4287670057 title "Discovering Textual Structures: Generative Grammar Induction using Template Trees" @default.
- W4287670057 doi "https://doi.org/10.48550/arxiv.2009.04530" @default.
- W4287670057 hasPublicationYear "2020" @default.
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