Jordan Lachler


2023

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Strengthening Relationships Between Indigenous Communities, Documentary Linguists, and Computational Linguists in the Era of NLP-Assisted Language Revitalization
Darren Flavelle | Jordan Lachler
Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

As the global crisis of language endangerment deepens, Indigenous communities have continued to seek new means of preserving, promoting and passing on their languages to future generations. For many communities, modern language technology holds the promise of accelerating that process. However, the cultural and disciplinary divides between documentary linguists, computational linguists and Indigenous communities have posed an on-going challenge for the development and deployment of NLP applications that can support the documentation and revitalization of Indigenous languages. In this paper, we discuss the main barriers to collaboration that these groups have encountered, as well as some notable initiatives in recent years to bring the groups closer together. We follow this with specific recommendations to build upon those efforts, calling for increased opportunities for awareness-building and skills-training in computational linguistics, tailored to the specific needs of both documentary linguists and Indigenous community members. We see this as an essential step as we move forward into an era of NLP-assisted language revitalization.

2022

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Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages
Sarah Moeller | Antonios Anastasopoulos | Antti Arppe | Aditi Chaudhary | Atticus Harrigan | Josh Holden | Jordan Lachler | Alexis Palmer | Shruti Rijhwani | Lane Schwartz
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

2021

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Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)
Antti Arppe | Jeff Good | Atticus Harrigan | Mans Hulden | Jordan Lachler | Sarah Moeller | Alexis Palmer | Miikka Silfverberg | Lane Schwartz
Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2019

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Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)
Antti Arppe | Jeff Good | Mans Hulden | Jordan Lachler | Alexis Palmer | Lane Schwartz | Miikka Silfverberg
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2018

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Modeling Northern Haida Verb Morphology
Jordan Lachler | Lene Antonsen | Trond Trosterud | Sjur Moshagen | Antti Arppe
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages
Antti Arppe | Jeff Good | Mans Hulden | Jordan Lachler | Alexis Palmer | Lane Schwartz
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

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A Morphological Parser for Odawa
Dustin Bowers | Antti Arppe | Jordan Lachler | Sjur Moshagen | Trond Trosterud
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

2016

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Training & Quality Assessment of an Optical Character Recognition Model for Northern Haida
Isabell Hubert | Antti Arppe | Jordan Lachler | Eddie Antonio Santos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We are presenting our work on the creation of the first optical character recognition (OCR) model for Northern Haida, also known as Masset or Xaad Kil, a nearly extinct First Nations language spoken in the Haida Gwaii archipelago in British Columbia, Canada. We are addressing the challenges of training an OCR model for a language with an extensive, non-standard Latin character set as follows: (1) We have compared various training approaches and present the results of practical analyses to maximize recognition accuracy and minimize manual labor. An approach using just one or two pages of Source Images directly performed better than the Image Generation approach, and better than models based on three or more pages. Analyses also suggest that a character’s frequency is directly correlated with its recognition accuracy. (2) We present an overview of current OCR accuracy analysis tools available. (3) We have ported the once de-facto standardized OCR accuracy tools to be able to cope with Unicode input. Our work adds to a growing body of research on OCR for particularly challenging character sets, and contributes to creating the largest electronic corpus for this severely endangered language.

2014

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Modeling the Noun Morphology of Plains Cree
Conor Snoek | Dorothy Thunder | Kaidi Lõo | Antti Arppe | Jordan Lachler | Sjur Moshagen | Trond Trosterud
Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages