I Speak for the Árboles: Developing a Dependency Treebank for Spanish L2 and Heritage Speakers

We introduce the first dependency treebank containing Universal Dependencies (UD) annotations for Spanish learner writing from the UC Davis COWSL2H corpus.Our annotations include lemmatization, POS tagging, and syntactic dependencies.We adapt the existing UD framework for Spanish L1 to account for learner-specific features such as code-switching and non-canonical syntax.A suite of parsing evaluation experiments shows that parsers trained on learner data together with moderate sizes of Spanish L1 data can yield reasonable performance.Our annotations are openly accessible to motivate future development of learner-oriented language technologies.

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Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.acl-srw.56
Primary Topic
Natural Language Processing Techniques
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article
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article

I Speak for the Árboles: Developing a Dependency Treebank for Spanish L2 and Heritage Speakers

Robert Pugh, Zoey Liu, E Pulido
Natural Language Processing Techniques
article

I Speak for the Árboles: Developing a Dependency Treebank for Spanish L2 and Heritage Speakers

Robert Pugh, Zoey Liu, E Pulido
article en

Abstract

We introduce the first dependency treebank containing Universal Dependencies (UD) annotations for Spanish learner writing from the UC Davis COWSL2H corpus.Our annotations include lemmatization, POS tagging, and syntactic dependencies.We adapt the existing UD framework for Spanish L1 to account for learner-specific features such as code-switching and non-canonical syntax.A suite of parsing evaluation experiments shows that parsers trained on learner data together with moderate sizes of Spanish L1 data can yield reasonable performance.Our annotations are openly accessible to motivate future development of learner-oriented language technologies.

Center for Applied Linguistics (US), Indiana University Bloomington (US), Institute of Linguistics (CN), Institute of Linguistics (RU)
Sustainable cities and communities
Openalex Percentile: Top 90%
Natural Language Processing Techniques
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