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Found 19 papers, 4 papers with code
Findings of the VarDial Evaluation Campaign 2021
This paper describes the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2021.
The LiLaH Emotion Lexicon of Croatian, Dutch and Slovene
We further showcase the usage of the lexicons by calculating the difference in emotion distributions in texts containing and not containing socially unacceptable discourse, comparing them across four languages (English, Croatian, Dutch, Slovene) and two topics (migrants and LGBT).
Cultural Topic Modelling over Novel Wikipedia Corpora for South-Slavic Languages
There is a shortage of high-quality corpora for South-Slavic languages.
MultiLexNorm: A Shared Task on Multilingual Lexical Normalization
This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation.
Sesame Street to Mount Sinai: BERT-constrained character-level Moses models for multilingual lexical normalization
This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization.
HeLju@VarDial 2020: Social Media Variety Geolocation with BERT Models
This paper describes the Helsinki-Ljubljana contribution to the VarDial shared task on social media variety geolocation.
A Report on the VarDial Evaluation Campaign 2020
This paper presents the results of the VarDial Evaluation Campaign 2020 organized as part of the seventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2020.
Exploring Stylometric and Emotion-Based Features for Multilingual Cross-Domain Hate Speech Detection
In this paper, we describe experiments designed to evaluate the impact of stylometric and emotion-based features on hate speech detection: the task of classifying textual content into hate or non-hate speech classes.
BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian
In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains.
Social Media Variety Geolocation with geoBERT
This paper describes the Helsinki–Ljubljana contribution to the VarDial 2021 shared task on social media variety geolocation.
Geographic Adaptation of Pretrained Language Models
Geographic linguistic features are commonly used to improve the performance of pretrained language models (PLMs) on NLP tasks where geographic knowledge is intuitively beneficial (e. g., geolocation prediction and dialect feature prediction).
The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild
This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1, 125 crawled Slovenian web documents that consist of 650 thousand words.
BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian
In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains.
Findings of the 2020 Conference on Machine Translation (WMT20)
In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories.
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
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.
The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English
In this paper we present datasets of Facebook comment threads to mainstream media posts in Slovene and English developed inside the Slovene national project FRENK which cover two topics, migrants and LGBT, and are manually annotated for different types of socially unacceptable discourse (SUD).
KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning
This paper presents a dataset and supervised learning experiments for term extraction from Slovene academic texts.
Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings
We show that the notions of concreteness and imageability are highly predictable both within and across languages, with a moderate loss of up to 20% in correlation when predicting across languages.
Bleaching Text: Abstract Features for Cross-lingual Gender Prediction
Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform-dependent.
