Papers with Slovenian

5 papers
TermoUD - a language-independent terminology extraction tool (2023.eacl-demo)

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Challenge: TermoUD is a language-independent terminology extraction tool . it uses languagedependent shallow grammar to select candidate terms .
Approach: They propose a language-independent terminology extraction tool called TermoUD which uses shallow grammar to select candidate terms.
Outcome: The proposed method is suitable for languages with the Universal Dependencies (UD) parser.
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? (2022.lrec-1)

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Challenge: Keyword extraction is the task of retrieving words that are essential to the content of a document.
Approach: They propose to use pretrained multilingual language models for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data.
Outcome: The proposed models outperform state-of-the-art unsupervised methods on low-resource languages with limited or no training data.
Cross-lingual Named Entity Corpus for Slavic Languages (2024.lrec-main)

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Challenge: This work presents a corpus manually annotated with named entities for six Slavic languages .
Approach: They propose to manually annotate a corpus of names for six Slavic languages . they use a transformer-based neural network architecture to train multilingual models .
Outcome: The corpus consists of 5,017 documents on seven topics . each entity is described by a category, a lemma, and a unique cross-lingual identifier.
A multilabel approach to morphosyntactic probing (2021.findings-emnlp)

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Challenge: Morphologically rich languages present unique challenges to natural language processing . morphological supervision can improve the quality of multilingual language models .
Approach: They propose a multilabel probing task to assess morphosyntactic representations of multilingual word embeddings.
Outcome: The proposed probing task makes it easy to explore morphosyntactic representations . it also allows the study of how language models handle co-occurring features .
Common Voice: A Massively-Multilingual Speech Corpus (2020.lrec-1)

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Challenge: Common Voice is a massively-multilingual collection of transcribed speech intended for speech technology research and development.
Approach: They propose to use Mozilla’s DeepSpeech Speech-to-Text toolkit to perform multilingual automatic speech recognition experiments.
Outcome: The proposed corpus is the largest in the public domain for speech recognition, both in terms of hours and languages.

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