Papers by Maria Valentini
On the Automatic Generation and Simplification of Children’s Stories (2023.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have made it possible to generate children's educational texts with appropriate lexical and readability levels. |
| Approach: | They first examine the ability of several popular LLMs to generate stories with properly adjusted lexical and readability levels. |
| Outcome: | The proposed models can generalize to the domain of children's stories and create an efficient pipeline for their automatic generation. |
Massively Multilingual Joint Segmentation and Glossing (2026.acl-long)
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Michael Ginn, Lindia Tjuatja, Enora Rice, Ali Marashian, Maria Valentini, Jasmine Xu, Graham Neubig, Alexis Palmer
| Challenge: | Existing models generate morpheme-level glosses but assign them to whole words without predicting the actual morphological boundaries, making them less interpretable and therefore untrustworthy to human annotators. |
| Approach: | They propose to use neural networks to predict interlinear glosses and morphological segmentation from raw text. |
| Outcome: | The proposed model outperforms GlossLM on glossing and beats open-source models on segmentation, glossing, and alignment. |