Lennart Wachowiak


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Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing
Lennart Wachowiak | Dagmar Gromann
Proceedings of the 29th International Conference on Computational Linguistics

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.


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Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains
Christian Lang | Lennart Wachowiak | Barbara Heinisch | Dagmar Gromann
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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CogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation Identification
Lennart Wachowiak | Christian Lang | Barbara Heinisch | Dagmar Gromann
Proceedings of the Workshop on the Cognitive Aspects of the Lexicon

We describe our submission to the CogALex-VI shared task on the identification of multilingual paradigmatic relations building on XLM-RoBERTa (XLM-R), a robustly optimized and multilingual BERT model. In spite of several experiments with data augmentation, data addition and ensemble methods with a Siamese Triple Net, Translrelation, the XLM-R model with a linear classifier adapted to this specific task, performed best in testing and achieved the best results in the final evaluation of the shared task, even for a previously unseen language.