Julian Hitschler


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A Dataset and Reranking Method for Multimodal MT of User-Generated Image Captions
Shigehiko Schamoni | Julian Hitschler | Stefan Riezler
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods
Vivi Nastase | Julian Hitschler
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Nematus: a Toolkit for Neural Machine Translation
Rico Sennrich | Orhan Firat | Kyunghyun Cho | Alexandra Birch | Barry Haddow | Julian Hitschler | Marcin Junczys-Dowmunt | Samuel Läubli | Antonio Valerio Miceli Barone | Jozef Mokry | Maria Nădejde
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-performing submissions to shared translation tasks at WMT and IWSLT, and has been used to train systems for production environments.

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Authorship Attribution with Convolutional Neural Networks and POS-Eliding
Julian Hitschler | Esther van den Berg | Ines Rehbein
Proceedings of the Workshop on Stylistic Variation

We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict the authors of scientific publications in the same discipline at levels well above chance.


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Multimodal Pivots for Image Caption Translation
Julian Hitschler | Shigehiko Schamoni | Stefan Riezler
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)