Malta

Dehumanization is a pernicious process of denying some or all attributes of humanness to the target group. It is frequently cited as a common hallmark of incitement to commit genocide. The international security landscape has seen a dramatic shift following the 2022 Russian invasion of Ukraine. This, coupled with recent developments in the conceptualization of dehumanization, necessitates the creation of new techniques for analyzing and detecting this extreme violence-related phenomenon on a large scale. Our project pioneers the development of a detection system for instances of dehumanization. To achieve this, we collected the entire posting history of the most popular bloggers on Russian Telegram and tested classical machine learning, deep learning, and zero-shot learning approaches to explore and detect the dehumanizing rhetoric. We found that the transformer-based method for entity extraction SpERT shows a promising result of F 1 = 0.85 for binary classification. The proposed methods can be built into the systems of anticipatory governance, contribute to the collection of evidence of genocidal intent in the Russian invasion of Ukraine, and pave the way for large-scale studies of dehumanizing language. This paper contains references to language that some readers may find offensive.

EACL 2024

Computational Analysis of Dehumanization of Ukrainians on Russian Social Media

russo-ukrainian war

genocidal speech

dehumanization detection

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 is a short paper describing the process of derivation of synthetic Judeo-French text. Judeo-French is one of a number of rare languages used in speaking and writing by Jewish communities as confined to a particular temporal and geographical frame (in this case, 11th- to 14th-century France). The number of resources in the language is very limited and its involvement in the contemporary domain of Natural Language Processing (NLP) is practically non-existent. This work outlines the compilation of a synthetic Judeo-French corpus. For the purpose, a pipeline of transformations is applied to Old French text belonging to the same general time period, leading to the derivation of text that is as reliable as possible in terms of phonological, morphological and lexical characteristics as witnessed in Judeo-French. Ultimately, the goal is for this synthetic corpus to be used in standard NLP tasks, such as Neural Machine Translation (NMT), as an instance of data augmentation.

Compilation of a Synthetic Judeo-French Corpus

In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document.

Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with Fourier Analysis

Multi-Word Expressions (MWEs) play a pivotal role in language use overall and in register formation more specifically, e.g. encoding field-specific terminology. Our study focuses on the identification and categorization of MWEs used in scientific writing, considering their formal characteristics as well as their developmental trajectory over time from the mid-17th century to the present. For this, we develop an approach combining three different types of methods to identify MWEs (Universal Dependency annotation, Partitioner and the Academic Formulas List) and selected measures to characterize MWE properties (e.g., dispersion by Kullback-Leibler Divergence and several association measures). This allows us to inspect MWEs types in a novel data-driven way regarding their functions and change over time in specialized discourse.

Multi-word Expressions in English Scientific Writing

The Moravians are a Christian group that has emerged from a 15th century movement. In this paper, we investigate how memoirs written by the devotees of this group can be analyzed with methods from computational linguistics, in particular sentiment analysis. To this end, we experiment with two different fine-tuning strategies and find that the best performance for ternary sentiment analysis (81% accuracy) is achieved by fine-tuning a German BERT model, outperforming in particular models trained on much larger German sentiment datasets. We further investigate the model(s) using SHAP scores and find that the best performing model struggles with multiple negations and mixed statements. Finally, we show two application scenarios motivated by research questions from religious studies.

Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting

We investigate the impact of the Plain English Movement (PEM) on the complexity of legal language in UK law reports from the 1950s-2010s, contrasting it with the evolution of scientific language. The PEM, emerging in the late 20th century, advocated for clear and understandable legal language. We define complexity through the concept of surprisal - an information-theoretic measure correlating with cognitive processing difficulty. Our research contrasts surprisal with traditional readability measures, which often overlook content. We hypothesize that, if the PEM has influenced legal language, there would be a reduction in complexity over time and a shift from a nominal to a more verbal style. We analyze text complexity and lexico-grammatical changes in line with PEM recommendations. Results indicate minimal impact of the PEM on both legal and scientific domains. This finding suggests future research should consider processing effort when advocating for linguistic norms to enhance accessibility.

Applying Information-theoretic Notions to Measure Effects of the Plain English Movement on English Law Reports and Scientific Articles

The pressing need for digitization of historical documents has led to a strong interest in designing computerised image processing methods for automatic handwritten text recognition. However, not much attention has been paid on studying the handwritten text written in the margins, i.e. marginalia, that also forms an important source of information. Nevertheless, training an accurate and robust recognition system for marginalia calls for data-efficient approaches due to the unavailability of sufficient amounts of annotated multi-writer texts. Therefore, this work presents an end-to-end framework for automatic detection and recognition of handwritten marginalia, and leverages data augmentation and transfer learning to overcome training data scarcity. The detection phase involves investigation of R-CNN and Faster R-CNN networks. The recognition phase includes an attention-based sequence-to-sequence model, with ResNet feature extraction, bidirectional LSTM-based sequence modeling, and attention-based prediction of marginalia. The effectiveness of the proposed framework has been empirically evaluated on the data from early book collections found in the Uppsala University Library in Sweden. Source code and pre-trained models are available at Github.

Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition

In this paper, we bridge computational linguistics with historical methods to explore the potential of topic modeling in historical newspapers. Our case study focuses on British and American newspapers published in the second half of the 20th century that debate issues of Greek tourism, but our method can be transposed to any diachronic data. We demonstrate that Non-negative Matrix Factorization (NFM) can generate interpretable topics within the historical period under examination providing a tangible example of how computational text analysis can assist historical research. The contribution of our work is two-fold; first, the extracted topics are evaluated both by a computational linguist and by a historian highlighting the crucial role of domain experts when interpreting topic modeling outputs. Second, the extracted topics are contextualized within the historical and political environment in which they appear, providing interesting insights about the historical representations of Greek tourism over the years, and about the development and the hallmarks of American and British tourism in Greece across different historical periods (from 1945 to 1989). The comparative analysis between the American and the British press reveals interesting insights including similar responses to specific events as well as notable differences between British and American tourism to Greece during the historical periods under examination. Overall, the results of our analysis can provide valuable information for academics and researchers in the field of (Digital) Humanities and Social Sciences, as well as for stakeholders in the tourism industry.

Historical Portrayal of Greek Tourism through Topic Modeling on International Newspapers

The quality of automatic transcription of heritage documents, whether from printed, manuscripts or audio sources, has a decisive impact on the ability to search and process historical texts. Although significant progress has been made in text recognition (OCR, HTR, ASR), textual materials derived from library and archive collections remain largely erroneous and noisy. Effective post-transcription correction methods are therefore necessary and have been intensively researched for many years. As large language models (LLMs) have recently shown exceptional performances in a variety of text-related tasks, we investigate their ability to amend poor historical transcriptions. We evaluate fourteen foundation language models against various post-correction benchmarks comprising different languages, time periods and document types, as well as different transcription quality and origins. We compare the performance of different model sizes and different prompts of increasing complexity in zero and few-shot settings. Our evaluation shows that LLMs are anything but efficient at this task. Quantitative and qualitative analyses of results allow us to share valuable insights for future work on post-correcting historical texts with LLMs.

Post-Correction of Historical Text Transcripts with Large Language Models: An Exploratory Study

Recent approaches to automatically detect the speaker of an utterance of direct speech often disregard general information about characters in favor of local information found in the context, such as surrounding mentions of entities. In this work, we explore stylistic representations of characters built by encoding their quotes with off-the-shelf pretrained authorship verification models in a large corpus of English novels (the Project Dialogism Novel Corpus). Results suggest that the combination of stylistic and topical information captured in these models accurately distinguish characters among each other, but does not necessarily improve over semantic-only models when attributing quotes. However, these results vary among novels and more investigation of stylometric models particularly tailored for literary texts and the study of characters should be conducted.

Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution

This study extends previous research on literary quality by using information theory-based methods to assess the level of perplexity recorded by three large language models when processing 20th-century English novels deemed to have high literary quality, recognized by experts as canonical, compared to a broader control group. We find that canonical texts appear to elicit a higher perplexity in the models, we explore which textual features might concur to create such an effect. We find that the usage of a more heavily nominal style, together with a more diverse vocabulary, is one of the leading causes of the difference between the two groups. These traits could reflect ``strategies'' to achieve an informationally dense literary style.

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