Marek Šuppa

Also published as: Marek Suppa


2024

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SlovakSum: A Large Scale Slovak Summarization Dataset
Viktoria Ondrejova | Marek Suppa
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

The ability to automatically summarize news articles has become increasingly important due to the vast amount of information available online. Together with the rise of chatbots , Natural Language Processing (NLP) has recently experienced a tremendous amount of development. Despite these advancements, the majority of research is focused on established well-resourced languages, such as English. To contribute to development of the low resource Slovak language, we introduce SlovakSum, a Slovak news summarization dataset consisting of over 200 thousand news articles with titles and short abstracts obtained from multiple Slovak newspapers. The abstractive approach, including MBART and mT5 models, was used to evaluate various baselines. The code for the reproduction of our dataset and experiments can be found at https://github.com/NaiveNeuron/slovaksum

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Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
Stephen Mayhew | Terra Blevins | Shuheng Liu | Marek Suppa | Hila Gonen | Joseph Marvin Imperial | Börje Karlsson | Peiqin Lin | Nikola Ljubešić | Lester James Miranda | Barbara Plank | Arij Riabi | Yuval Pinter
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 19 datasets annotated with named entities in a cross-lingual consistent schema across 13 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We will release the data, code, and fitted models to the public.

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Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA
Marek Suppa | Daniel Skala | Daniela Jass | Samuel Sucik | Andrej Svec | Peter Hraska
Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)

This study details our approach for the CASE 2024 Shared Task on Climate Activism Stance and Hate Event Detection, focusing on Hate Speech Detection, Hate Speech Target Identification, and Stance Detection as classification challenges. We explored the capability of Large Language Models (LLMs), particularly GPT-4, in zero- or few-shot settings enhanced by retrieval augmentation and re-ranking for Tweet classification. Our goal was to determine if LLMs could match or surpass traditional methods in this context. We conducted an ablation study with LLaMA for comparison, and our results indicate that our models significantly outperformed the baselines, securing second place in the Target Detection task. The code for our submission is available at https://github.com/NaiveNeuron/bryndza-case-2024

2023

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WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition
David Suba | Marek Suppa | Jozef Kubik | Endre Hamerlik | Martin Takac
Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)

Named Entity Recognition (NER) is a fundamental NLP tasks with a wide range of practical applications. The performance of state-of-the-art NER methods depends on high quality manually anotated datasets which still do not exist for some languages. In this work we aim to remedy this situation in Slovak by introducing WikiGoldSK, the first sizable human labelled Slovak NER dataset. We benchmark it by evaluating state-of-the-art multilingual Pretrained Language Models and comparing it to the existing silver-standard Slovak NER dataset. We also conduct few-shot experiments and show that training on a sliver-standard dataset yields better results. To enable future work that can be based on Slovak NER, we release the dataset, code, as well as the trained models publicly under permissible licensing terms at https://github.com/NaiveNeuron/WikiGoldSK

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Prompterator: Iterate Efficiently towards More Effective Prompts
Samuel Sučik | Daniel Skala | Andrej Švec | Peter Hraška | Marek Šuppa
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

With the advent of Large Language Models (LLMs) the process known as prompting, which entices the LLM to solve an arbitrary language processing task without the need for finetuning, has risen to prominence. Finding well-performing prompts, however, is a non-trivial task which requires experimentation in order to arrive at a prompt that solves a specific task. When a given task does not readily reduce to one that can be easily measured with well established metrics, human evaluation of the results obtained by prompting is often necessary. In this work we present prompterator, a tool that helps the user interactively iterate over various potential prompts and choose the best performing one based on human feedback. It is distributed as an open source package with out-of-the-box support for various LLM providers and was designed to be easily extensible.

2021

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Cost-effective Deployment of BERT Models in Serverless Environment
Marek Suppa | Katarína Benešová | Andrej Švec
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

In this study, we demonstrate the viability of deploying BERT-style models to AWS Lambda in a production environment. Since the freely available pre-trained models are too large to be deployed in this environment, we utilize knowledge distillation and fine-tune the models on proprietary datasets for two real-world tasks: sentiment analysis and semantic textual similarity. As a result, we obtain models that are tuned for a specific domain and deployable in the serverless environment. The subsequent performance analysis shows that this solution does not only report latency levels acceptable for production use but that it is also a cost-effective alternative to small-to-medium size deployments of BERT models, all without any infrastructure overhead.

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Benchmarking Pre-trained Language Models for Multilingual NER: TraSpaS at the BSNLP2021 Shared Task
Marek Suppa | Ondrej Jariabka
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

In this paper we describe TraSpaS, a submission to the third shared task on named entity recognition hosted as part of the Balto-Slavic Natural Language Processing (BSNLP) Workshop. In it we evaluate various pre-trained language models on the NER task using three open-source NLP toolkits: character level language model with Stanza, language-specific BERT-style models with SpaCy and Adapter-enabled XLM-R with Trankit. Our results show that the Trankit-based models outperformed those based on the other two toolkits, even when trained on smaller amounts of data. Our code is available at https://github.com/NaiveNeuron/slavner-2021.

2020

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A Summarization Dataset of Slovak News Articles
Marek Suppa | Jergus Adamec
Proceedings of the Twelfth Language Resources and Evaluation Conference

As a well established NLP task, single-document summarization has seen significant interest in the past few years. However, most of the work has been done on English datasets. This is particularly noticeable in the context of evaluation where the dominant ROUGE metric assumes its input to be written in English. In this paper we aim to address both of these issues by introducing a summarization dataset of articles from a popular Slovak news site and proposing small adaptation to the ROUGE metric that make it better suited for Slovak texts. Several baselines are evaluated on the dataset, including an extractive approach based on the Multilingual version of the BERT architecture. To the best of our knowledge, the presented dataset is the first large-scale news-based summarization dataset for text written in Slovak language. It can be reproduced using the utilities available at https://github.com/NaiveNeuron/sme-sum