| Challenge: | Existing systems for learning morphology have limited their use to languages with publicly available structured data, such as online dictionaries like Wiktionary. |
| Approach: | They propose a task that generates entire morphological paradigms from IGT input and a language expert cleaning noisy IGT data. |
| Outcome: | The proposed task speeds up the process and generates entire morphological paradigm tables from IGT input. |
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| Challenge: | Documentation is not a cure-all for language loss, but it is an important part of language preservation. |
| Approach: | They propose to use multi-source neural models to create automatic glossing models . they also explore cross-lingual transfer and a simple output length control mechanism . |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource scenarios. |
Can we teach language models to gloss endangered languages? (2024.findings-emnlp)
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| Challenge: | Prior research has explored statistical and neural methods for automatically producing IGT. |
| Approach: | They propose to use in-context learning to generate interlinear glossed text . they propose to employ supervised learning to select examples to provide in-text . |
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GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text (2024.emnlp-main)
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| 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. |
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Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Under-Documented Languages (2022.findings-acl)
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Clarissa Forbes, Farhan Samir, Bruce Oliver, Changbing Yang, Edith Coates, Garrett Nicolai, Miikka Silfverberg
| Challenge: | Recent progress in NLP is driven by pretrained models leveraging massive datasets. |
| Approach: | They argue that IGT data can be leveraged provided target language expertise is available and that it can be used to create effective models. |
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Wav2Gloss: Generating Interlinear Glossed Text from Speech (2024.acl-long)
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Taiqi He, Kwanghee Choi, Lindia Tjuatja, Nathaniel Robinson, Jiatong Shi, Shinji Watanabe, Graham Neubig, David Mortensen, Lori Levin
| Challenge: | Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for endangered languages. |
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Morphological Inflection: A Reality Check (2023.acl-long)
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| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
| Outcome: | The proposed methods improve generalizability and reliability of results and improve generalization abilities. |
Automated Parsing of Interlinear Glossed Text from Page Images of Grammatical Descriptions (2020.lrec-1)
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| Challenge: | linguistic typology is a subfield of linguistics which studies the design features of human language and the distribution of such features across the languages of the world. |
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A Computational Model for the Linguistic Notion of Morphological Paradigm (C18-1)
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| Challenge: | In supervised learning of morphological patterns, the strategy of generalizing inflectional tables into more abstract paradigms has been proposed as an efficient method to deduce the inflection of unseen word forms. |
| Approach: | They propose to generalize inflectional tables into more abstract paradigms by aligning the longest common subsequence found in an inflection table with the longest lexeme. |
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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. |
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Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation (2025.emnlp-main)
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| 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. |
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