Malta

Identifying hate speech is a challenging specialization in the natural language processing field (NLP). Particularly in fields with differing linguistics, it becomes more demanding to construct a well-performing classifier for the betterment of the community. In this paper, we leveraged the performances of pre-trained models on the given hate speech detection dataset. By conducting a hyperparameter search, we computed the feasible setups for fine-tuning and trained effective classifiers that performed well in both subtasks in the HSD-2Lang 2024 contest.

EACL 2024

ReBERT at HSD-2Lang 2024: Fine-Tuning BERT with AdamW for Hate Speech Detection in Arabic and Turkish

hatespeech

fine-tuning

bert

Welcome to the 18th 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 has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. 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. 
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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.

workshop paper

This paper addresses hate speech detection in Turkish and Arabic tweets, contributing to the HSD-2Lang Shared Task. We propose a specialized pooling strategy within a soft-voting ensemble framework to improve classification in Turkish and Arabic language models. Our approach also includes expanding the training sets through cross-lingual translation, introducing a broader spectrum of hate speech examples. Our method attains F1-Macro scores of 0.6964 for Turkish (Subtask A) and 0.7123 for Arabic (Subtask B). While achieving these results, we also consider the computational overhead, striking a balance between the effectiveness of our unique pooling strategy, data augmentation, and soft-voting ensemble. This approach advances the practical application of language models in low-resource languages for hate speech detection.

DetectiveReDASers at HSD-2Lang 2024: A New Pooling Strategy with Cross-lingual Augmentation and Ensembling for Hate Speech Detection in Low-resource Languages

The use of hate speech targeting ethnicity, nationalities, religious identities, and specific groups has been on the rise in the news media. However, most existing automatic hate speech detection models focus on identifying hate speech, often neglecting the target group-specific language that is common in news articles. To address this problem, we first compile a hate speech dataset, TurkishHatePrintCorpus, derived from Turkish news articles and annotate it specifically for the language related to the targeted group. We then introduce the HateTargetBERT model, which integrates the target-centric linguistic features extracted in this study into the BERT model, and demonstrate its effectiveness in detecting hate speech while allowing the model's classification decision to be explained. We have made the dataset and source code publicly available at https://github.com/boun-tabi/HateTargetBERT-TR.

Detecting Hate Speech in Turkish Print Media: A Corpus and A Hybrid Approach with Target-oriented Linguistic Knowledge

Addressing the need for effective hate speech moderation in contemporary digital discourse, the Multimodal Hate Speech Event Detection Shared Task made its debut at CASE 2023, co-located with RANLP 2023. Building upon its success, an extended version of the shared task was organized at the CASE workshop in EACL 2024. Similar to the earlier iteration, in this shared task, participants address hate speech detection through two subtasks. Subtask A is a binary classification problem, assessing whether text-embedded images contain hate speech. Subtask B goes further, demanding the identification of hate speech targets, such as individuals, communities, and organizations within text-embedded images. Performance is evaluated using the macro F1-score metric in both subtasks. With a total of 73 registered participants, the shared task witnessed remarkable achievements, with the best F1-scores in Subtask A and Subtask B reaching 87.27 and 80.05, respectively, surpassing the leaderboard of the previous CASE 2023 shared task. This paper provides a comprehensive overview of the performance of seven teams that submitted results for Subtask A and five teams for Subtask B.

Extended Multimodal Hate Speech Event Detection During Russia-Ukraine Crisis - Shared Task at CASE 2024

This paper offers an overview of Hate Speech Detection in Turkish and Arabic Tweets (HSD-2Lang) Shared Task at CASE workshop to be held jointly with EACL 2024. The task was divided into two subtasks: Subtask A, targeting hate speech detection in various Turkish contexts, and Subtask B, addressing hate speech detection in Arabic with limited data. The shared task attracted significant attention with 33 teams that registered and 10 teams that participated in at least one task. In this paper, we provide the details of the tasks and the approaches adopted by the participant along with an analysis of the results obtained from this shared task.

Overview of the Hate Speech Detection in Turkish and Arabic Tweets (HSD-2Lang) Shared Task at CASE 2024

Social media plays a pivotal role in global discussions, including on climate change. The variety of opinions expressed range from supportive to oppositional, with some instances of hate speech. Recognizing the importance of understanding these varied perspectives, the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) at EACL 2024 hosted a shared task focused on detecting stances and hate speech in climate activism-related tweets. This task was divided into three subtasks: subtasks A and B concentrated on identifying hate speech and its targets, while subtask C focused on stance detection. Participants'' performance was evaluated using the macro F1-score. With over 100 teams participating, the highest F1 scores achieved were 91.44 in subtask C, 78.58 in subtask B, and 74.83 in subtask A. This paper details the methodologies of 24 teams that submitted their results to the competition''s leaderboard.

Stance and Hate Event Detection in Tweets Related to Climate Activism - Shared Task at CASE 2024

Generative AI systems aim to create customizable content for their users, with a subsequent surge in demand for adaptable tools that can create personalized experiences. 

This paper presents HumSum, a web-based tool tailored for humanities students to effectively summarize their lecture transcripts and to personalize the summaries to their specific needs. We first conducted a survey driven by different potential scenarios to collect user preferences to guide the implementation of this tool. 

Utilizing Streamlit, we crafted the user interface, while Langchain's Map Reduce function facilitated the summarization process for extensive lectures using OpenAI's GPT-4 model. HumSum is an intuitive tool serving various summarization needs, infusing personalization into the tool's functionality without requiring personal user data collection.

HumSum: A Personalized Lecture Summarization Tool for Humanities Students Using LLMs

The quantity and quality of data have a significant impact on the performance of artificial intelligence (AI). Consequently, there is a growing interest in synthetic data generation for medical AI. However, research has primarily focused on medical images, with little given to text-based data such as medical records. This is because in the biomedical field, text data often contains sensitive information such as personal information, making it difficult to access a sufficient amount of data required for medical AI. Therefore, this study explores the application of language models (LMs) for synthetic text generation in low-resource domains like medical records. It compares the results of synthetic text generation based on different LMs. To achieve this, we focused on two criteria for LM-based synthetic text generation of medical records using two keywords entered by the user: 1) the impact of the LM's knowledge, 2) the impact of the LM's size. Additionally, we objectively evaluated the generated synthetic text, including quantitative metrics such as BLUE and ROUGE, along with clinician's evaluations.

How to use Language Models for Synthetic Text Generation in Cerebrovascular Disease-specific Medical Reports

While both agent interaction and personalisa- tion are vibrant topics in research on large language models (LLMs), there has been lim- ited focus on the effect of language interac- tion on the behaviour of persona-conditioned LLM agents. Such an endeavour is impor- tant to ensure that agents remain consistent to their assigned traits yet are able to engage in open, naturalistic dialogues. In our exper- iments, we condition GPT-3.5 on personality profiles through prompting and create a two- group population of LLM agents using a simple variability-inducing sampling algorithm. We then administer personality tests and submit the agents to a collaborative writing task, find- ing that different profiles exhibit different de- grees of personality consistency and linguistic alignment to their conversational partners. Our study seeks to lay the groundwork for better understanding of dialogue-based interaction be- tween LLMs and highlights the need for new approaches to crafting robust, more human-like LLM personas for interactive environments.

LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models

Modeling long user histories plays a pivotal role in enhancing recommendation systems, allowing to capture users’ evolving preferences, resulting in more precise and personalized recommendations. In this study, we tackle the challenges of modeling long user histories for preference understanding in natural language. Specifically, we introduce a new User Embedding Module (UEM) that efficiently processes user history in free-form text by compressing and representing them as embeddings, to use them as soft prompts to a LM. Our experiments demonstrate the superior capability of this approach in handling significantly longer histories compared to conventional text-based methods, yielding substantial improvements in predictive performance. Models trained using our approach exhibit substantial enhancements, with up to 0.21 and 0.25 F1 points improvement over the text-based prompting baselines. The main contribution of this research is to demonstrate the ability to bias language models via user signals.

User Embedding Model for Personalized Language Prompting

With the rising popularity of LLMs in the public sphere, they have become more and more attractive as a tool for doing one’s own research without having to rely on search engines or specialized knowledge of a scientific field. But using LLMs as a source for factual information can lead one to fall prey to misinformation or hallucinations dreamed up by the model. In this paper we examine the gpt-4 LLM by simulating a large number of potential research queries and evaluate how many of the generated references are factually correct as well as existent.

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DetectiveReDASers at HSD-2Lang 2024: A New Pooling Strategy with Cross-lingual Augmentation and Ensembling for Hate Speech Detection in Low-resource Languages

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