114 citations · 315 across the 19 of their papers we have counts for
11 papers · 1 filter
Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategies
Boshko Koloski, Blaž Škrlj, Marko Robnik-Šikonja +1
The cross-lingual transfer is a promising technique to solve tasks in less-resourced languages. In this empirical study, we compare two fine-tuning approaches combined with zero-sh…
Enhancing deep neural networks with morphological information
Matej Klemen, Luka Krsnik, Marko Robnik-Šikonja
Deep learning approaches are superior in NLP due to their ability to extract informative features and patterns from languages. The two most successful neural architectures are LSTM…
Bayesian Methods for Semi-supervised Text Annotation
Kristian Miok, Gregor Pirs, Marko Robnik-Sikonja
Human annotations are an important source of information in the development of natural language understanding approaches. As under the pressure of productivity annotators can assig…
MICE: Mining Idioms with Contextual Embeddings
Tadej Škvorc, Polona Gantar, Marko Robnik-Šikonja
Idiomatic expressions can be problematic for natural language processing applications as their meaning cannot be inferred from their constituting words. A lack of successful method…
FinEst BERT and CroSloEngual BERT: less is more in multilingual models
Matej Ulčar, Marko Robnik-Šikonja
Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. The research has been mostly focused on English language, though. While massiv…
Cross-lingual Transfer of Sentiment Classifiers
Marko Robnik-Sikonja, Kristjan Reba, Igor Mozetic
Word embeddings represent words in a numeric space so that semantic relations between words are represented as distances and directions in the vector space. Cross-lingual word embe…
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
Carlos Santos Armendariz, Matthew Purver, Matej Ulčar +5
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. Stand…
High Quality ELMo Embeddings for Seven Less-Resourced Languages
Matej Ulčar, Marko Robnik-Šikonja
Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task. We offer pre…
Multilingual Culture-Independent Word Analogy Datasets
Matej Ulčar, Kristiina Vaik, Jessica Lindström +2
In text processing, deep neural networks mostly use word embeddings as an input. Embeddings have to ensure that relations between words are reflected through distances in a high-di…
Prediction Uncertainty Estimation for Hate Speech Classification
Kristian Miok, Dong Nguyen-Doan, Blaž Škrlj +2
As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large comm…
Supervised and Unsupervised Neural Approaches to Text Readability
Matej Martinc, Senja Pollak, Marko Robnik-Šikonja
We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language model…