Matúš Pikuliak


SlovakBERT: Slovak Masked Language Model
Matúš Pikuliak | Štefan Grivalský | Martin Konôpka | Miroslav Blšták | Martin Tamajka | Viktor Bachratý | Marian Simko | Pavol Balážik | Michal Trnka | Filip Uhlárik
Findings of the Association for Computational Linguistics: EMNLP 2022

We introduce a new Slovak masked language model called SlovakBERT. This is to our best knowledge the first paper discussing Slovak transformers-based language models. We evaluate our model on several NLP tasks and achieve state-of-the-art results. This evaluation is likewise the first attempt to establish a benchmark for Slovak language models. We publish the masked language model, as well as the fine-tuned models for part-of-speech tagging, sentiment analysis and semantic textual similarity.

Average Is Not Enough: Caveats of Multilingual Evaluation
Matúš Pikuliak | Marian Simko
Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)

This position paper discusses the problem of multilingual evaluation. Using simple statistics, such as average language performance, might inject linguistic biases in favor of dominant language families into evaluation methodology. We argue that a qualitative analysis informed by comparative linguistics is needed for multilingual results to detect this kind of bias. We show in our case study that results in published works can indeed be linguistically biased and we demonstrate that visualization based on URIEL typological database can detect it.


STUFIIT at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter with MUSE and ELMo Embeddings
Michal Bojkovský | Matúš Pikuliak
Proceedings of the 13th International Workshop on Semantic Evaluation

We present a number of models used for hate speech detection for Semeval 2019 Task-5: Hateval. We evaluate the viability of multilingual learning for this task. We also experiment with adversarial learning as a means of creating a multilingual model. Ultimately our multilingual models have had worse results than their monolignual counterparts. We find that the choice of word representations (word embeddings) is very crucial for deep learning as a simple switch between MUSE and ELMo embeddings has shown a 3-4% increase in accuracy. This also shows the importance of context when dealing with online content.


Improving Moderation of Online Discussions via Interpretable Neural Models
Andrej Švec | Matúš Pikuliak | Marián Šimko | Mária Bieliková
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappropriate comments for moderators to see. Second, we highlight inappropriate parts within these comments to make the moderation faster. We evaluated our method on data from a major Slovak news discussion platform.