Papers by Enora Rice
From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing data for low-resource languages are limited; the languages that could most benefit from domain adaptation (DA) are the ones left behind. |
| Approach: | They propose a realistic setting in which they aim to translate between a high-resource and a low-resourced language with limited parallel data, a bilingual dictionary, and c) a monolingual target-domain corpus in the high-rsource language. |
| Outcome: | The proposed methods are compared with a human evaluation of DALI and show that the most effective is the simplest. |
Massively Multilingual Joint Segmentation and Glossing (2026.acl-long)
Copied to clipboard
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. |
Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation (2025.emnlp-main)
Copied to clipboard
| Challenge: | despite interest in language documentation, we still lack broadly usable tools that support workflows. |
| Approach: | They propose to integrate user-centered design principles into NLP to reshape the field. |
| Outcome: | The proposed model fails to meet core usability needs in real-world language documentation contexts. |
GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing resources for standardized, easily accessible IGT data limit their applicability to linguistic research. |
| Approach: | They compile the largest existing corpus of interlinear glossed text data from a variety of sources and use it to generate annotated text. |
| Outcome: | The proposed model outperforms SOTA models on monolingual corpora by 6.6%. |
TAMS: Translation-Assisted Morphological Segmentation (2024.acl-long)
Copied to clipboard
| Challenge: | Canonical morphological segmentation is a key task in endangered language documentation . training data for canonical segmentation can be difficult, making it difficult to train high quality models. |
| Approach: | They propose a model that leverages translation data to speed up canonical segmentation . they propose to use translation data as an additional signal to leverage the data . |
| Outcome: | The proposed model outperforms baseline models in a super-low resource setting but yields mixed results on training splits with more data. |