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Found 11 papers, 2 papers with code
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
EMBEDDIA hackathon report: Automatic sentiment and viewpoint analysis of Slovenian news corpus on the topic of LGBTIQ+
We conduct automatic sentiment and viewpoint analysis of the newly created Slovenian news corpus containing articles related to the topic of LGBTIQ+ by employing the state-of-the-art news sentiment classifier and a system for semantic change detection.
Fusion of linguistic, neural and sentence-transformer features for improved term alignment
Crosslingual terminology alignment task has many practical applications.
Sequence to sequence pretraining for a less-resourced Slovenian language
Large pretrained language models have recently conquered the area of natural language processing.
Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages
To analyze the importance of focusing on a single language and the importance of a large training set, we compare created models with existing monolingual and multilingual BERT models for Estonian, Latvian, and Lithuanian.
Evaluation of contextual embeddings on less-resourced languages
The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives.
Cross-lingual alignments of ELMo contextual embeddings
Building machine learning prediction models for a specific NLP task requires sufficient training data, which can be difficult to obtain for less-resourced languages.
FinEst BERT and CroSloEngual BERT: less is more in multilingual models
Large pretrained masked language models have become state-of-the-art solutions for many NLP problems.
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists.
Multilingual Culture-Independent Word Analogy Datasets
In text processing, deep neural networks mostly use word embeddings as an input.
High Quality ELMo Embeddings for Seven Less-Resourced Languages
Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task.
