Typology

What is “Typological Diversity” in NLP?
The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for …
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A Principled Framework for Evaluating on Typologically Diverse Languages
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform …
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Multilingual Gradient Word-Order Typology from Universal Dependencies
While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological …
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A Call for Consistency in Reporting Typological Diversity
In order to draw generalizable conclusions about the performance of multilingual models across languages, it is important to evaluate …
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Typological Challenges for the Application of Multilingual Language Models in the Digital Humanities
This chapter explores the challenges faced by multilingual digital humanities (DH) researchers when using natural language processing …
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The Past, Present, and Future of Typological Databases in NLP
Typological information has the potential to be beneficial in the development of NLP models, particularly for low-resource languages. …
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The Role of Typological Feature Prediction in NLP and Linguistics
Computational typology has gained traction in the field of Natural Language Processing (NLP) in recent years, as evidenced by the …
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Colex2Lang: Language Embeddings from Semantic Typology
In semantic typology, colexification refers to words with multiple meanings, either related (polysemy) or unrelated (homophony). …
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Gradual Language Model Adaptation Using Fine-Grained Typology
Transformer-based language models (LMs) offer superior performance in a wide range of NLP tasks compared to previous paradigms. …
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