Suna Bensch


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Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case
Adam Dahlgren Lindström | Johanna Björklund | Suna Bensch | Frank Drewes
Proceedings of the 28th International Conference on Computational Linguistics

Semantic embeddings have advanced the state of the art for countless natural language processing tasks, and various extensions to multimodal domains, such as visual-semantic embeddings, have been proposed. While the power of visual-semantic embeddings comes from the distillation and enrichment of information through machine learning, their inner workings are poorly understood and there is a shortage of analysis tools. To address this problem, we generalize the notion ofprobing tasks to the visual-semantic case. To this end, we (i) discuss the formalization of probing tasks for embeddings of image-caption pairs, (ii) define three concrete probing tasks within our general framework, (iii) train classifiers to probe for those properties, and (iv) compare various state-of-the-art embeddings under the lens of the proposed probing tasks. Our experiments reveal an up to 16% increase in accuracy on visual-semantic embeddings compared to the corresponding unimodal embeddings, which suggest that the text and image dimensions represented in the former do complement each other.


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Predicting User Competence from Linguistic Data
Yonas Woldemariam | Henrik Björklund | Suna Bensch
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)


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Incremental Construction of Millstream Configurations Using Graph Transformation
Suna Bensch | Frank Drewes | Helmut Jürgensen | Brink van der Merwe
Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing


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Millstream Systems – a Formal Model for Linking Language Modules by Interfaces
Suna Bensch | Frank Drewes
Proceedings of the 2010 Workshop on Applications of Tree Automata in Natural Language Processing