Search Results for author: Sarah Ebling

Found 14 papers, 1 papers with code

The Myth of Signing Avatars

no code implementations MTSummit 2021 John C. McDonald, Rosalee Wolfe, Eleni Efthimiou, Evita Fontinea, Frankie Picron, Davy Van Landuyt, Tina Sioen, Annelies Braffort, Michael Filhol, Sarah Ebling, Thomas Hanke, Verena Krausneker

Development of automatic translation between signed and spoken languages has lagged behind the development of automatic translation between spoken languages, but it is a common misperception that extending machine translation techniques to include signed languages should be a straightforward process.

Machine Translation Translation

Benchmarking Automated Review Response Generation for the Hospitality Domain

no code implementations EcomNLP (COLING) 2020 Tannon Kew, Michael Amsler, Sarah Ebling

Online customer reviews are of growing importance for many businesses in the hospitality industry, particularly restaurants and hotels.

Domain Adaptation Response Generation

Exploring German Multi-Level Text Simplification

1 code implementation RANLP 2021 Nicolas Spring, Annette Rios, Sarah Ebling

We report on experiments in automatic text simplification (ATS) for German with multiple simplification levels along the Common European Framework of Reference for Languages (CEFR), simplifying standard German into levels A1, A2 and B1.

Text Simplification

Real-Time Sign Language Detection using Human Pose Estimation

no code implementations11 Aug 2020 Amit Moryossef, Ioannis Tsochantaridis, Roee Aharoni, Sarah Ebling, Srini Narayanan

We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing.

Optical Flow Estimation Pose Estimation

Benchmarking Data-driven Automatic Text Simplification for German

no code implementations LREC 2020 Andreas S{\"a}uberli, Sarah Ebling, Martin Volk

Automatic text simplification is an active research area, and there are first systems for English, Spanish, Portuguese, and Italian.

Machine Translation Text Simplification +1

A Corpus for Automatic Readability Assessment and Text Simplification of German

no code implementations LREC 2020 Alessia Battisti, Sarah Ebling

In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification of German.

Text Simplification

Helping Domain Experts Build Speech Translation Systems

no code implementations7 Oct 2015 Manny Rayner, Alejandro Armando, Pierrette Bouillon, Sarah Ebling, Johanna Gerlach, Sonia Halimi, Irene Strasly, Nikos Tsourakis

We present a new platform, "Regulus Lite", which supports rapid development and web deployment of several types of phrasal speech translation systems using a minimal formalism.

Sign Language Translation Translation

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