Michael Barz


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Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings
Marimuthu Kalimuthu | Michael Barz | Daniel Sonntag
Proceedings of the Fourth Arabic Natural Language Processing Workshop

We study the problem of incremental domain adaptation of a generic neural machine translation model with limited resources (e.g., budget and time) for human translations or model training. In this paper, we propose a novel query strategy for selecting “unlabeled” samples from a new domain based on sentence embeddings for Arabic. We accelerate the fine-tuning process of the generic model to the target domain. Specifically, our approach estimates the informativeness of instances from the target domain by comparing the distance of their sentence embeddings to embeddings from the generic domain. We perform machine translation experiments (Ar-to-En direction) for comparing a random sampling baseline with our new approach, similar to active learning, using two small update sets for simulating the work of human translators. For the prescribed setting we can save more than 50% of the annotation costs without loss in quality, demonstrating the effectiveness of our approach.


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A Multimodal Dialogue System for Medical Decision Support inside Virtual Reality
Alexander Prange | Margarita Chikobava | Peter Poller | Michael Barz | Daniel Sonntag
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

We present a multimodal dialogue system that allows doctors to interact with a medical decision support system in virtual reality (VR). We integrate an interactive visualization of patient records and radiology image data, as well as therapy predictions. Therapy predictions are computed in real-time using a deep learning model.