Implicitly abusive language (IAL), unlike its explicit counterpart, lacks overt slurs or unambiguously offensive keywords, such as “bimbo” or “scum”, making it challenging to detect and mitigate. While current research predominantly focuses on explicitly abusive language, the subtler and more covert forms of IAL remain insufficiently studied. The rapid advancement and widespread adoption of large language models (LLMs) have opened new possibilities for various NLP tasks, but their application to IAL detection has been limited. We revisit three very recent challenging datasets of IAL and investigate the potential of LLMs to enhance the detection of IAL in English through zero-shot and few-shot prompting approaches. We evaluate the models’ capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL. Our results indicate that classifiers trained on features extracted by advanced LLMs outperform the best previously reported results, achieving near-human performance.

In today's globalized and increasingly technological world, skills in transcultural communication and multilingual language technology development–ranging from speech recognition to machine translation–are qualifications highly demanded in the job market. The interdisciplinary field of language technology and computational linguistics equips university students with these essential skills. However, in Austria a study program focusing on this interdisciplinary field was missing. The Master's program Multilingual Technologies, now offered jointly by the University of Vienna and the University of Applied Sciences Campus Vienna, aims to bridge the gap between language and technology while fostering expertise in multilingual applications. This paper explores the program's interdisciplinary approach, highlighting its design, collaborative framework, and its implications for both academia and the job market. We also present first students' evaluations results, which indicate a high satisfaction among students and possible room for future improvements.

Understanding the relation between the meanings of words is an important part of comprehending natural language. Prior work has either focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs), with some exceptions. Given the rarity of highly multilingual benchmarks, it is unclear to what extent PLMs capture relational knowledge and are able to transfer it across languages. To start addressing this question, we propose MultiLexBATS, a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages, such as Bambara, Lithuanian, and Albanian. As experiment on cross-lingual transfer of relational knowledge, we test the PLMs’ ability to (1) capture analogies across languages, and (2) predict translation targets. We find considerable differences across relation types and languages with a clear preference for hypernymy and antonymy as well as romance languages.

With advances in the field of Linked (Open) Data (LOD), language data on the LOD cloud has grown in number, size, and variety. With an increased volume and variety of language data, optimizations of methods for distributing, storing, and querying these data become more central. To this end, this position paper investigates use cases at the intersection of LLOD and Big Data, existing approaches to utilizing Big Data techniques within the context of linked data, and discusses the challenges and benefits of this union.

Translation quality and its assessment are of great importance in the context of human as well as machine translation. Methods range from human annotation and assessment to quality metrics and estimation, where the former are rather time-consuming. Furthermore, assessing translation quality is a subjective process. Best-Worst Scaling (BWS) represents a time-efficient annotation method to obtain subjective preferences, the best and the worst in a given set and their ratings. In this paper, we propose to use BWS for a comparative translation quality assessment of one human and three machine translations to German of the same source text in English. As a result, ten participants with a translation background selected the human translation most frequently and rated it overall as best closely followed by DeepL. Participants showed an overall positive attitude towards this assessment method.

Conceptual metaphors present a powerful cognitive vehicle to transfer knowledge structures from a source to a target domain.Prior neural approaches focus on detecting whether natural language sequences are metaphoric or literal.We believe that to truly probe metaphoric knowledge in pre-trained language models, their capability to detect this transfer should be investigated.To this end, this paper proposes to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor's source domain without any pre-set domains.We experiment with different training sample configurations for fine-tuning and few-shot prompting on two distinct datasets.When provided 12 fewshot samples in the prompt, GPT-3 generates the correct source domain for a new sample with an accuracy of 65.15% in English and 34.65% in Spanish.GPT's most common error is a hallucinated source domain for which no indicator is present in the sentence.Other common errors include identifying a sequence as literal even though a metaphor is present and predicting the wrong source domain based on specific words in the sequence that are not metaphorically related to the target domain.

Quality assurance is a central component of human and machine translation. In translation studies, translation quality focuses on human evaluation and dimensions, such as purpose, comprehensibility, target audience among many more. Within the field of machine translation, more operationalized definitions of quality lead to automated metrics relying on reference translations or quality estimation. A joint approach to defining and assessing translation quality holds the promise to be mutually beneficial. To contribute towards that objective, this systematic survey provides an interdisciplinary analysis of the concept of translation quality from both perspectives. Thereby, it seeks to inspire cross-fertilization between both fields and further development of an interdisciplinary concept of translation quality.

Machine Translation (MT) models are well-known to suffer from gender bias, especially for gender beyond a binary conception. Due to the multiplicity of language-specific strategies for gender representation beyond the binary, debiasing MT is extremely challenging. As an alternative, we propose a case study on gender-fair post-editing. In this study, six professional translators each post-edited three English to German machine translations. For each translation, participants were instructed to use a different gender-fair language strategy, that is, gender-neutral rewording, gender-inclusive characters, and a neosystem. The focus of this study is not on translation quality but rather on the ease of integrating gender-fair language into the post-editing process. Findings from non-participant observation and interviews show clear differences in temporal and cognitive effort between participants and strategy as well as in the success of using gender-fair language.

In embodied cognition, physical experiences are believed to shape abstract cognition, such as natural language and reasoning. Image schemas were introduced as spatio-temporal cognitive building blocks that capture these recurring sensorimotor experiences. The few existing approaches for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. Furthermore, the lack of sufficiently large, annotated datasets makes evaluation and supervised learning difficult. We propose to build on the recent success of large multilingual pretrained language models and a small dataset of examples from image schema literature to train a supervised classifier that classifies natural language expressions of varying lengths into image schemas. Despite most of the training data being in English with few examples for German, the model performs best in German. Additionally, we analyse the model’s zero-shot performance in Russian, French, and Mandarin. To further investigate the model’s behaviour, we utilize local linear approximations for prediction probabilities that indicate which words in a sentence the model relies on for its final classification decision. Code and dataset are publicly available.

In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We rst introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We de ne under-resourced languages with a speci c focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources.

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.