Friday, February 3, 2017 - 9:30am

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Towards Breaking the Language and Modality Barrier:  Learning Cross-lingual Cross-modal Semantic Representations

ABSTRACT  Information retrieval and machine learning approaches are running in the background of most of the applications we use in our daily digital life. The assistance they are providing is manifold, but relies on a set of core content processing tasks requiring compatible representation formalisms. However, this is rarely the case in real-world scenarios.

This talk is concerned with shared representation formalisms for information encoded in heterogeneous modalities. The heterogeneity may result from intra-modal varieties, like text in different languages for the modality of natural language, or by the different modalities themselves, like when relating text to images or to knowledge graphs. 

I will discuss three ways to obtain a joint representation of heterogeneously represented content. The first one is based on explicit semantics as encoded in knowledge graphs, the second one extends this approach by adding implicit semantics extracted from large data sets and the final one relies on joint learning without utilizing explicit semantics.

The presented approaches contribute to the long standing challenges of braking the language and modality barriers in order to enable the joint semantic processing of content in originally incompatible representation formalisms.

BIO  Achim Rettinger is a KIT Junior Research Group Leader at AIFB where he is heading the Adaptive Data Analytics team.  His research areas include Data Mining, Information Extraction, Knowledge Discovery, Ontology Learning, Machine Learning, Human Computer Systems, and Text Mining.

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