Papers by Dagmar Gromann
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe (2020.lrec-1)
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Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez-Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim Köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiș, Tamás Váradi, Kadri Vider, Andy Way, François Yvon
| Challenge: | Language Technologies (LTs) are a powerful means to break down language barriers impacting business, cross-lingual and cross-cultural communication in Europe. |
| Approach: | They present an overview of the European LT landscape and the current state of play in industry and the LT market. |
| Outcome: | The present study outlines 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. |
Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings (2025.coling-main)
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| Challenge: | Current research focuses on explicit abusive language, but subtler forms of IAL remain insufficiently studied. |
| Approach: | They evaluate the models' capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL. |
| Outcome: | The proposed models outperform the best previously reported methods in classifying sentences directly as IAL or benign and extracting linguistic features associated with IAL. |
Cross-Lingual Link Discovery for Under-Resourced Languages (2022.lrec-1)
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Michael Rosner, Sina Ahmadi, Elena-Simona Apostol, Julia Bosque-Gil, Christian Chiarcos, Milan Dojchinovski, Katerina Gkirtzou, Jorge Gracia, Dagmar Gromann, Chaya Liebeskind, Giedrė Valūnaitė Oleškevičienė, Gilles Sérasset, Ciprian-Octavian Truică
| Challenge: | Linked data paradigms can be used to solve under-resourced languages' problem of under-utilization of resources. |
| Approach: | They propose a paradigm for cross-lingual link discovery that can be applied to under-resourced languages . they argue that techniques for cross language linking can be readily applied . |
| Outcome: | The proposed technologies can be applied to under-resourced languages, the authors argue . the authors show that the Linked Data paradigm can be used to solve the problem . |
Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains (2021.findings-acl)
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| Challenge: | Automated Term Extraction (ATE) is a challenging task, with few exceptions. |
| Approach: | They propose to use a transformer-based term extraction model to extract terms from sentences . they also propose to employ a language model for token classification and a sequence model to reduce sentences to terms . |
| Outcome: | The proposed models outperform baselines on the ATE challenge TermEval 2020 dataset in English, French, and Dutch. |
Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing (2022.coling-1)
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| Challenge: | Existing methods for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. |
| Approach: | They propose to train a supervised classifier that classifies natural language expressions into image schemas using a large dataset of examples from image schema literature. |
| Outcome: | The proposed model performs best in German, Russian, and French, and is based on a small dataset of examples from image schema literature. |
Word-Level Detection of Code-Mixed Hate Speech with Multilingual Domain Transfer (2025.findings-acl)
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| Challenge: | a growing problem in language detection tasks is code-mixing, a combination of more than one language . lack of available datasets for code-mixing causes the problem . authors propose a multilingual approach to code-matching . |
| Approach: | They propose to use an annotated hate speech dataset to detect code-mixing in profane language . they propose to apply bilingual fine-tuned models to code-mixed hate speech in german rap lyrics . |
| Outcome: | The proposed model can detect code-mixed hate speech and neologisms in German rap lyrics . the proposed model is more nuanced than binary classification . |
From Linguistic Linked Data to Big Data (2024.lrec-main)
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Dimitar Trajanov, Elena Apostol, Radovan Garabik, Katerina Gkirtzou, Dagmar Gromann, Chaya Liebeskind, Cosimo Palma, Michael Rosner, Alexia Sampri, Gilles Sérasset, Blerina Spahiu, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene
| Challenge: | Language data on the LOD cloud has grown in number, size, and variety . Linked (Open) Data (LLOD) is a standardized way of representing and sharing linguistic datasets . |
| Approach: | They propose to combine LLOD and Big Data to improve interoperability of linguistic datasets . they propose to use a machine-readable format to represent and share linguistic data . |
| Outcome: | This paper examines the use cases of Linked (Open) Data and Big Data in language data. |
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)
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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| Challenge: | Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions. |
| Approach: | They propose to use 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 an experiment on cross-lingual transfer of relational knowledge. |
| Outcome: | The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian. |
Does GPT-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models (2023.acl-long)
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| Challenge: | Existing approaches to detect whether natural language sequences are metaphoric or literal focus on detecting the transfer of knowledge structures to pre-trained language models. |
| Approach: | They propose to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor’s source domain without any pre-set domains. |
| Outcome: | The proposed model generates the correct source domain for a new sample with an accuracy of 65.15% in English and 34.65% in Spanish. |
Comparing Pretrained Multilingual Word Embeddings on an Ontology Alignment Task (L18-1)
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| Challenge: | Existing word embeddings capture a string's semantics and can be trained for multiple languages. |
| Approach: | They propose to compare three different multilingual pretrained word embedding repositories with a string-matching baseline and use it to compute semantic similarities of strings in different languages. |
| Outcome: | The proposed method produces correct alignments on a non-standard dataset on all four languages. |