diplomsko delo
Žan Pečovnik (Author), Marko Robnik Šikonja (Mentor), Nikola Ljubešić (Co-mentor)

Abstract

Z razvojem družbenih omrežij je narasla pogostost sovražnega govora v upo- rabniških vsebinah. Osredotočili se bomo na dve trenutno najbolj aktualni temi, LGBT in migrante. Za napovedovanje sovražnega govora bomo upo- rabili nevronsko mrežo BERT in naredili primerjavo med večjezikovnim mo- delom, ki je naučen na 104 različnih jezikih ter trojezikovnim modelom, ki je naučen na slovenščini, hrvaščini in angleščini. Ugotovili smo, da trojezikovni model za približno 5% natančneje napoveduje sovražni govor na jeziku, na katerem je bil model tudi naučen. Večjezikovni model, brez ali z dodatnim učenjem, natančneje kot trojezikovni model napoveduje sovražni govor na jezikih, na katerem prvotno model ni bil naučen. To kaže na boljši medjezi- kovni prenos večjezikovnega napovednega modela.

Keywords

sovražni govor;model BERT;nevronske mreže;medjezikovni prenos;strojno učenje;obdelava naravnega jezika;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [Ž. Pečovnik]
UDC: 004.85(043.2)
COBISS: 18357251 Link will open in a new window
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Other data

Secondary language: English
Secondary title: Cross-lingual transfer of hate speech prediction models
Secondary abstract: With the development of social networks, there has been a signicant in- crease of hate speech in user generated contents. We focus on two most discussed topics, LGBT and migrants. We use the BERT neural network for prediction of hate speech and make a comparison between the multilingual model, trained on 104 dierent languages, and a trilingual model, trained on Slovene, Croatian and English. Results show that the trilingual model is ap- proximately 5% more accurate predicting hate speech on a language that it was trained on. The multilingual model with or without additional training is more accurate on languages that it was not trained on. This indicates a better cross-lingual transfer of multilingual model.
Secondary keywords: hate speech;BERT model;neural networks;cross-lingual transfer;machine learning;natural language processing;computer and information science;diploma;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 28 str.
ID: 11779600