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Found 10 papers, 5 papers with code
Zero-Shot Cross-Lingual Transfer in Legal Domain Using Transformer Models
Also, Gradual unfreezing of pre-trained model's layers during training results in relative improvement of 38-45% for French and 58-70% for German.
Large Scale Legal Text Classification Using Transformer Models
Large multi-label text classification is a challenging Natural Language Processing (NLP) problem that is concerned with text classification for datasets with thousands of labels.
Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural Architectures
Generating coherent, grammatically correct, and meaningful text is very challenging, however, it is crucial to many modern NLP systems.
A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data
With the growing number and size of Linked Data datasets, it is crucial to make the data accessible and useful for users without knowledge of formal query languages.
Word Similarity Datasets for Thai: Construction and Evaluation
In this work, we create three Thai word similarity datasets by translating and re-rating the popular WordSim-353, SimLex-999 and SemEval-2017-Task-2 datasets.
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain
Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task.
Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata
One of the big challenges in Linked Data consumption is to create visual and natural language interfaces to the data usable for non-technical users.
Russian Language Datasets in the Digitial Humanities Domain and Their Evaluation with Word Embeddings
In this paper, we present Russian language datasets in the digital humanities domain for the evaluation of word embedding techniques or similar language modeling and feature learning algorithms.
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings
In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models.
Extracting Social Networks from Literary Text with Word Embedding Tools
In this paper a social network is extracted from a literary text.
