Croatia

Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.

EACL 2023

Cross-Cultural Transfer Learning for Chinese Offensive Language Detection

cross-cultural

transfer learning

offensive language detection

## Welcome to EMNLP 2022!
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workshop paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

We present a cross-lingual study of homo- transphobia on Twitter, examining the preva- lence and forms of homotransphobic content in tweets related to LGBT issues in seven languages. Our findings reveal that homotransphobia is a global problem that takes on distinct cultural expressions, influenced by factors such as misinformation, cultural prejudices, and religious beliefs. To aid the detection of hate speech, we also devise a taxonomy that classifies public discourse around LGBT issues. By contributing to the growing body of research on online hate speech, our study provides valuable insights for creating effective strategies to combat homotransphobia on social media.

A Cross-Lingual Study of Homotransphobia on Twitter

Social media plays a significant role in cross-cultural communication. A vast amount of this occurs in code-mixed and multilingual form, posing a significant challenge to Natural Language Processing (NLP) tools for processing such information, like language identification, topic modeling, and named-entity recognition. To address this, we introduce a large-scale multilingual and multi-topic dataset (MMT) collected from Twitter (≈1.7 million Tweets), encompassing 13 coarse-grained and 63 fine-grained topics in the Indian context. We further annotate a subset of 5,346 tweets from the MMT dataset with various Indian languages and their code-mixed counterparts. Also, we demonstrate that the currently existing tools fail to capture the linguistic diversity in MMT on two downstream tasks, i.e., topic modeling and language identification. To facilitate future research, we will make the anonymized and annotated dataset available in the public domain.

MMT: A Multilingual and Multi-Topic Indian Social Media Dataset

Measurements of fairness in NLP have been critiqued for lacking concrete definitions of biases or harms measured, and for perpetuating a singular, Western narrative of fairness globally. To combat some of these pivotal issues, methods for curating datasets and benchmarks that target specific harms are rapidly emerging. However, these methods still face the significant challenge of achieving coverage over global cultures and perspectives at scale. To address this, in this paper, we highlight the utility and importance of complementary approaches that leverage both large generative models as well as community engagement, in these curation strategies. We specifically target the harm of stereotyping and demonstrate a pathway to build a benchmark that covers stereotypes about diverse, and intersectional identities. We discuss the two approaches, their advantages and constraints, the characteristics of the data they produce, and finally, their potential to be used complementarily for better evaluation of stereotyping harms.

Building Stereotype Repositories with Complementary Approaches for Scale and Depth

The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.

Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments

Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms. We provide a novel experimental setup that applies personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms. We further provide an analysis of performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict, and assess where personalization helps the most.

Unifying Data Perspectivism and Personalization: An Application to Social Norms

In order for NLP technology to be widely applicable and useful, it needs to be inclusive of users across the world's languages, equitable, i.e., not unduly biased towards any particular language, and accessible to users, particularly in low-resource settings where compute constraints are common. In this paper, we propose an evaluation paradigm that assesses NLP technologies across all three dimensions, hence quantifying the diversity of users they can serve. While inclusion and accessibility have received attention in recent literature, equity is currently unexplored. We propose to address this gap using the Gini coefficient, a well-established metric used for estimating societal wealth inequality. Using our paradigm, we highlight the distressed state of diversity of current technologies for Indian (IN) languages, motivated by their linguistic diversity and large, varied speaker population. To improve upon these metrics, we demonstrate the importance of region-specific choices in model building and dataset creation and also propose a novel approach to optimal resource allocation during fine-tuning. Finally, we discuss steps that must be taken to mitigate these biases and call upon the community to incorporate our evaluation paradigm when building linguistically diverse technologies.

Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages

This study addresses the challenges of learning unsupervised word representations for the morphologically rich and low-resource Ukrainian language. Traditional models that perform decently on English do not generalize well for such languages due to a lack of sufficient data and the complexity of their grammatical structures. To overcome these challenges, we utilized a high-quality, large dataset of different genres for learning Ukrainian word vector representations. We found the best hyperparameters to train fastText language models on this dataset and performed intrinsic and extrinsic evaluations of the generated word embeddings using the established methods and metrics. The results of this study indicate that the trained vectors exhibit superior performance on intrinsic tests in comparison to existing embeddings for Ukrainian. Our best model gives 62% Accuracy on the word analogy task. Extrinsic evaluations were performed on two sequence labeling tasks: NER and POS tagging (83% spaCy NER F-score, 83% spaCy POS Accuracy, 92% Flair POS Accuracy).

Learning Word Embeddings for Ukrainian: A Comparative Study of FastText Hyperparameters

In the Russo-Ukrainian war, propaganda is produced by Russian state-run news outlets for both international and domestic audiences. Its content and form evolve and change with time as the war continues. This constitutes a challenge to content moderation tools based on machine learning when the data used for training and the current news start to differ significantly. In this follow-up study, we evaluate our previous BERT and SVM models that classify Pro-Kremlin propaganda from a Pro-Western stance, trained on the data from news articles and telegram posts at the start of 2022, on the new 2023 subset. We examine both classifiers' errors and perform a comparative analysis of these subsets to investigate which changes in narratives provoke drops in performance.

The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective

This paper addresses the need for massive corpora for a low-resource language and presents the publicly available UberText 2.0 corpus for the Ukrainian language and discusses the methodology of its construction. While the collection and maintenance of such a corpus is more of a data extraction and data engineering task, the corpus itself provides a solid foundation for natural language processing tasks. It can enable the creation of contemporary language models and word embeddings, resulting in a better performance of numerous downstream tasks for the Ukrainian language. In addition, the paper and software developed can be used as a guidance and model solution for other low-resource languages. The resulting corpus is available for download on the project page. It has 3.274 billion tokens, consists of 8.59 million texts and takes up 32 gigabytes of space.

Introducing UberText 2.0: A Corpus of Modern Ukrainian at Scale

This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Ukrainian language based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on the dataset generated in an unsupervised way to obtain better contextual embeddings for words with multiple senses. The paper presents a method for generating a new dataset for WSD evaluation in the Ukrainian language based on the SUM dictionary. We developed a comprehensive framework that facilitates the generation of WSD evaluation datasets, enables the use of different prediction strategies, LLMs, and pooling strategies, and generates multiple performance reports. Our approach shows 77,9% accuracy for lexical meaning prediction for homonyms.

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