% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@incollection{woodbury_2011,
  author    = {Anthony C. Woodbury},
  title     = {Language Documentation},
  booktitle = {The Cambridge Handbook of Endangered Languages},
  editor    = {Peter K. Austin and Julia Sallabank},
  pages     = {159--186},
  year      = {2011},
  publisher = {Cambridge University Press}
}

@article{sapien_using_2019,
	title = {Using existing documentation for teaching and learning endangered languages},
	volume = {33},
	issn = {0950-0782},
	doi = {10.1080/09500782.2019.1622711},
	pages = {560--576},
	number = {6},
	journaltitle = {Language and Education},
	author = {Sapién, Racquel-María and Hirata-Edds, Tracy},
	date = {2019-11},
	year={2019}
}

@misc{chaudhary_teacher_2022,
	title = {Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning},
	rights = {{arXiv}.org perpetual, non-exclusive license},
	url = {https://arxiv.org/abs/2206.05154},
	doi = {10.48550/ARXIV.2206.05154},
	abstract = {One of the challenges of language teaching is how to organize the rules regarding syntax, semantics, or phonology of the language in a meaningful manner. This not only requires pedagogical skills, but also requires a deep understanding of that language. While comprehensive materials to develop such curricula are available in English and some broadly spoken languages, for many other languages, teachers need to manually create them in response to their students' needs. This process is challenging because i) it requires that such experts be accessible and have the necessary resources, and ii) even if there are such experts, describing all the intricacies of a language is time-consuming and prone to omission. In this article, we present an automatic framework that aims to facilitate this process by automatically discovering and visualizing descriptions of different aspects of grammar. Specifically, we extract descriptions from a natural text corpus that answer questions about morphosyntax (learning of word order, agreement, case marking, or word formation) and semantics (learning of vocabulary) and show illustrative examples. We apply this method for teaching the Indian languages, Kannada and Marathi, which, unlike English, do not have well-developed pedagogical resources and, therefore, are likely to benefit from this exercise. To assess the perceived utility of the extracted material, we enlist the help of language educators from schools in North America who teach these languages to perform a manual evaluation. Overall, teachers find the materials to be interesting as a reference material for their own lesson preparation or even for learner evaluation.},
	publisher = {{arXiv}},
	author = {Chaudhary, Aditi and Sampath, Arun and Sheshadri, Ashwin and Anastasopoulos, Antonios and Neubig, Graham},
	urldate = {2025-09-25},
	date = {2022},
	note = {Version Number: 1},
	keywords = {Computation and Language (cs.{CL}), {FOS}: Computer and information sciences},
	year={2025}
}

@article{Zheng2025LogiDynamics,
  title={LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in Large Language Model Reasoning},
  author={Zheng, Tianshi and others},
  journal={arXiv preprint arXiv:2502.11176},
  year={2025},
  url={https://doi.org/10.48550/arXiv.2502.11176}
}

@article{nllb_team_scaling_2024,
	title = {Scaling neural machine translation to 200 languages},
	volume = {630},
	issn = {0028-0836, 1476-4687},
	url = {https://www.nature.com/articles/s41586-024-07335-x},
	doi = {10.1038/s41586-024-07335-x},
	year={2024},
	abstract = {Abstract
            
              The development of neural techniques has opened up new avenues for research in machine translation. Today, neural machine translation ({NMT}) systems can leverage highly multilingual capacities and even perform zero-shot translation, delivering promising results in terms of language coverage and quality. However, scaling quality {NMT} requires large volumes of parallel bilingual data, which are not equally available for the 7,000+ languages in the world
              1
              . Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind—a single massively multilingual model that leverages transfer learning across languages. We developed a conditional computational model based on the Sparsely Gated Mixture of Experts architecture
              2–7
              , which we trained on data obtained with new mining techniques tailored for low-resource languages. Furthermore, we devised multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. We evaluated the performance of our model over 40,000 translation directions using tools created specifically for this purpose—an automatic benchmark ({FLORES}-200), a human evaluation metric ({XSTS}) and a toxicity detector that covers every language in our model. Compared with the previous state-of-the-art models, our model achieves an average of 44\% improvement in translation quality as measured by {BLEU}. By demonstrating how to scale {NMT} to 200 languages and making all contributions in this effort freely available for non-commercial use, our work lays important groundwork for the development of a universal translation system.},
	pages = {841--846},
	number = {8018},
	journaltitle = {Nature},
	shortjournal = {Nature},
	author = {{NLLB Team} and Costa-jussà, Marta R. and Cross, James and Çelebi, Onur and Elbayad, Maha and Heafield, Kenneth and Heffernan, Kevin and Kalbassi, Elahe and Lam, Janice and Licht, Daniel and Maillard, Jean and Sun, Anna and Wang, Skyler and Wenzek, Guillaume and Youngblood, Al and Akula, Bapi and Barrault, Loic and Gonzalez, Gabriel Mejia and Hansanti, Prangthip and Hoffman, John and Jarrett, Semarley and Sadagopan, Kaushik Ram and Rowe, Dirk and Spruit, Shannon and Tran, Chau and Andrews, Pierre and Ayan, Necip Fazil and Bhosale, Shruti and Edunov, Sergey and Fan, Angela and Gao, Cynthia and Goswami, Vedanuj and Guzmán, Francisco and Koehn, Philipp and Mourachko, Alexandre and Ropers, Christophe and Saleem, Safiyyah and Schwenk, Holger and Wang, Jeff},
	urldate = {2025-12-09},
	date = {2024-06-27},
	langid = {english},
	file = {Full Text:/Users/sebastienchristian/Zotero/storage/PT6S4YZI/NLLB Team et al. - 2024 - Scaling neural machine translation to 200 languages.pdf:application/pdf},
}

@article{kachinske_interaction_2019,
	title = {The interaction between timing of explicit grammar explanation and individual differences in second language acquisition},
	volume = {2},
	issn = {2542-3835},
	doi = {10.1075/jsls.19003.kac},
	abstract = {{\textless}p{\textgreater}Despite numerous positive findings of explicit instruction, this topic continues to engage scholars worldwide. One issue that may be crucial for the effectiveness of explicit instruction is the interaction between cognitive individual differences (language aptitude and working memory) and types of instruction. In this experiment, 128 learners of Spanish were randomly assigned to four experimental treatments and completed comprehension-based practice for interpreting object-verb and ser/estar sentences in Spanish. Results revealed that the various combinations of rules and practice posed differential task demands on the learners and consequently drew on language aptitude and working memory to a different extent. We argue that not only are rules and practice both necessary, but that their suitable integration ameliorates task demands, reducing the burden on the learner, and accordingly mitigates the role of participants’ individual differences, thus making a substantial difference for the learning of second language grammar.{\textless}/p{\textgreater}},
	pages = {197--232},
	number = {2},
	journaltitle = {Journal of Second Language Studies},
	author = {Kachinske, Ilina and {DeKeyser}, Robert},
	date = {2019-10},
	year={2019}
}

@article{nabizadeh_effect_2016,
	title = {The Effect of Implicit / Explicit Instruction on Learning English Grammar},
	journaltitle = {Modern Journal of Language Teaching Methods},
	author = {Nabizadeh, A. and Taghinezhad, A. and Azizi, Maral},
	date = {2016},
	year={2016}
}

@inproceedings{black_paws_2009,
	title = {{PAWS}: Parser and Writer for Syntax, Drafting Syntactic Grammars in the Third Wave},
	booktitle = {{SIL} Forum for Language Fieldwork},
	author = {Black, Cheryl and Black, Andrew},
	editor = {International, S. I. L.},
	date = {2009},
	year={2009}
}

@article{howell_building_2022,
	title = {Building Analyses from Syntactic Inference in Local Languages: An {HPSG} Grammar Inference System},
	volume = {8},
	issn = {2000-1533},
	doi = {10.3384/nejlt.2000-1533.2022.4017},
	abstract = {{\textless}p{\textgreater}We present a grammar inference system that leverages linguistic knowledge recorded in the form of annotations in interlinear glossed text ({IGT}) and in a meta-grammar engineering system (the {LinGO} Grammar Matrix customization system) to automatically produce machine-readable {HPSG} grammars. Building on prior work to handle the inference of lexical classes, stems, affixes and position classes, and preliminary work on inferring case systems and word order, we introduce an integrated grammar inference system that covers a wide range of fundamental linguistic phenomena. System development was guided by 27 geneologically and geographically diverse languages, and we test the system's cross-linguistic generalizability on an additional 5 held-out languages, using datasets provided by field linguists. Our system out-performs three baseline systems in increasing coverage while limiting ambiguity and producing richer semantic representations, while also producing richer representations than previous work in grammar inference.{\textless}/p{\textgreater}},
	number = {1},
	journaltitle = {Northern European Journal of Language Technology},
	author = {Howell, Kristen and Bender, Emily M.},
	date = {2022-07},
	year={2022}
}

@software{christian_dig4el_2025,
  author    = {Christian, Sebastien},
  title     = {{DIG4EL}},
  year      = {2026},
  version   = {1.1.2},
  publisher = {Sebastien Christian},
  doi       = {10.5281/zenodo.19465517},
  url       = {https://doi.org/10.5281/zenodo.19465517}
}

@inproceedings{corro_sparse_2024,
	title = {Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks},
	abstract = {Descriptive grammars are highly valuable, but writing them is time-consuming and difficult. Furthermore, while linguists typically use corpora to create them, grammar descriptions often lack quantitative data. As for formal grammars, they can be challenging to interpret. In this paper, we propose a new method to extract and explore significant fine-grained grammar patterns and potential syntactic grammar rules from treebanks, in order to create an easy-to-understand corpus-based grammar. More specifically, we extract descriptions and rules across different languages for two linguistic phenomena, agreement and word order, using a large search space and paying special attention to the ranking order of the extracted rules. For that, we use a linear classifier to extract the most salient features that predict the linguistic phenomena under study. We associate statistical information to each rule, and we compare the ranking of the model`s results to those of other quantitative and statistical measures. Our method captures both well-known and less well-known significant grammar rules in Spanish, French, and Wolof.},
	pages = {15114--15125},
	booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC}-{COLING} 2024)},
	publisher = {{ELRA} and {ICCL}},
	author = {Corro, Caio and Kahane, Sylvain},
	editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
	date = {2024},
	year={2024}
}

@misc{francois_proposal_2019,
	title = {A proposal for conversational questionnaires},
	url = {http://hdl.handle.net/10125/24861},
	publisher = {University of Hawai'i Press},
	author = {François, Alexandre},
	date = {2019},
	year = {2019}
}

@article{christian_enhancing_2025,
	title = {Enhancing grammatical documentation for endangered languages with graph-based meaning representation and Loopy Belief Propagation},
	volume = {12},
	issn = {29497191},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2949719125000408},
	doi = {10.1016/j.nlp.2025.100164},
	pages = {100164},
	journaltitle = {Natural Language Processing Journal},
	shortjournal = {Natural Language Processing Journal},
	author = {Christian, Sebastien},
	urldate = {2025-09-24},
	date = {2025-09},
	langid = {english},
	year={2025}
}

@misc{wals_online,
  author       = {Dryer, Matthew S. and Haspelmath, Martin},
  title        = {WALS Online},
  year         = {2013},
  howpublished = {\url{https://wals.info/}},
  note         = {Max Planck Institute for Evolutionary Anthropology, Leipzig. Accessed 25 February 2026}
}

@misc{grambank_clld_2023,
  author       = {Skirgård, Hedvig and Haynie, Hannah J. and Jang, Haejoong and 
                  Greenhill, Simon J. and Hammarström, Harald and Forkel, Robert and 
                  Bank, Sebastian and Bowern, Claire and Gray, Russell D. and Evans, Nicholas},
  title        = {Grambank 1.0},
  year         = {2023},
  howpublished = {\url{https://grambank.clld.org/}},
  note         = {Cross-Linguistic Data Formats (CLDF) database, CLLD platform. Accessed 25 February 2026}
}

@article{wilkinson_fair_2016,
	title = {The {FAIR} Guiding Principles for scientific data management and stewardship},
	volume = {3},
	issn = {2052-4463},
	doi = {10.1038/sdata.2016.18},
	abstract = {{\textless}p{\textgreater}There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the {FAIR} Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the {FAIR} Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the {FAIR} Principles, and includes the rationale behind them, and some exemplar implementations in the community.{\textless}/p{\textgreater}},
	pages = {160018},
	number = {1},
	journaltitle = {Scientific Data},
	author = {Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg, {IJsbrand} Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and Santos, Luiz Bonino da Silva and Bourne, Philip E. and Bouwman, Jildau and Brookes, Anthony J. and Clark, Tim and Crosas, Mercè and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J. G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa, Jaap and Hoen, Peter A. C. ’t and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and Schaik, Rene van and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A. and Thompson, Mark and Lei, Johan van der and Mulligen, Erik van and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
	date = {2016-03},
	year={2016}
}

@article{carroll_care_2020,
	title = {The {CARE} Principles for Indigenous Data Governance},
	volume = {19},
	issn = {1683-1470},
	doi = {10.5334/dsj-2020-043},
	journaltitle = {Data Science Journal},
	author = {Carroll, Stephanie Russo and Garba, Ibrahim and Figueroa-Rodríguez, Oscar L. and Holbrook, Jarita and Lovett, Raymond and Materechera, Simeon and Parsons, Mark and Raseroka, Kay and Rodriguez-Lonebear, Desi and Rowe, Robyn and Sara, Rodrigo and Walker, Jennifer D. and Anderson, Jane and Hudson, Maui},
	date = {2020-11},
	year={2020}
}

@misc{FLEx2025,
  author = {SIL},
  title = {FieldWorks Language Explorer (FLEx)},
  version = {9.1.18},
  year = {2025},
  url = {https://software.sil.org/fieldworks/},
}

