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Found 14 papers, 5 papers with code
How Universal is Metonymy? Results from a Large-Scale Multilingual Analysis
Metonymy is regarded by most linguists as a universal cognitive phenomenon, especially since the emergence of the theory of conceptual mappings.
SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe.
MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology
Large-scale morphological databases provide essential input to a wide range of NLP applications.
IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource
We introduce the IndoUKC, a new multilingual lexical database comprised of eighteen Indian languages, with a focus on formally capturing words and word meanings specific to Indian languages and cultures.
Layers of technology in pluriversal design. Decolonising language technology with the LiveLanguage initiative
Language technology is a complex and emerging field that presents challenges for co-design interventions due to enfolding in assemblages of global scale and diverse sites and its knowledge intensity.
Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge
Our empirical findings show that the accuracy of UniMorph Labeller is 98%, and that, in all language models studied (including ALBERT, BERT, RoBERTa, and DeBERTa), alien tokenization leads to poorer generalizations compared to morphological tokenization for semantic compositionality of word meanings.
Advancing the Arabic WordNet: Elevating Content Quality
High-quality WordNets are crucial for achieving high-quality results in NLP applications that rely on such resources.
Lexical Diversity in Kinship Across Languages and Dialects
The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages.
Towards Bridging the Digital Language Divide
It is a well-known fact that current AI-based language technology -- language models, machine translation systems, multilingual dictionaries and corpora -- focuses on the world's 2-3% most widely spoken languages.
Diversity and Language Technology: How Techno-Linguistic Bias Can Cause Epistemic Injustice
It is well known that AI-based language technology -- large language models, machine translation systems, multilingual dictionaries, and corpora -- is currently limited to 2 to 3 percent of the world's most widely spoken and/or financially and politically best supported languages.
The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections.
Ranked #8 on
Morpheme Segmentaiton
on UniMorph 4.0
UniMorph 4.0: Universal Morphology
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship
We capture the phenomenon of diversity through the notions of lexical gap and language-specific word and use a systematic method to infer gaps semi-automatically on a large scale.
Language Diversity: Visible to Humans, Exploitable by Machines
The Universal Knowledge Core (UKC) is a large multilingual lexical database with a focus on language diversity and covering over a thousand languages.
