Search Results for author: Eleftherios Avramidis

Found 27 papers, 3 papers with code

Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples

no code implementations SLTAT (LREC) 2022 Neha Deshpande, Fabrizio Nunnari, Eleftherios Avramidis

In this paper, we investigate the capability of convolutional neural networks to recognize in sign language video frames the six basic Ekman facial expressions for ‘fear’, ‘disgust’, ‘surprise’, ‘sadness’, ‘happiness’, ‘anger’ along with the ‘neutral’ class.

Data Augmentation Facial Expression Recognition (FER)

A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output

1 code implementation LREC 2022 Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, He Wang, Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, Hans Uszkoreit

Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese–English.

Machine Translation

Using Neural Machine Translation Methods for Sign Language Translation

1 code implementation ACL 2022 Galina Angelova, Eleftherios Avramidis, Sebastian Möller

We examine methods and techniques, proven to be helpful for the text-to-text translation of spoken languages in the context of gloss-to-text translation systems, where the glosses are the written representation of the signs.

Data Augmentation Machine Translation +3

Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena

no code implementations ACL 2021 Patrick Stadler, Vivien Macketanz, Eleftherios Avramidis

In this paper we present our observations and evaluations by observing the linguistic performance of the system on several steps on the training process of various English-to-German Neural Machine Translation models.

Machine Translation NMT +1

Fine-grained linguistic evaluation for state-of-the-art Machine Translation

no code implementations WMT (EMNLP) 2020 Eleftherios Avramidis, Vivien Macketanz, Ursula Strohriegel, Aljoscha Burchardt, Sebastian Möller

This paper describes a test suite submission providing detailed statistics of linguistic performance for the state-of-the-art German-English systems of the Fifth Conference of Machine Translation (WMT20).

Machine Translation Translation

Linguistic evaluation of German-English Machine Translation using a Test Suite

no code implementations WS 2019 Eleftherios Avramidis, Vivien Macketanz, Ursula Strohriegel, Hans Uszkoreit

We present the results of the application of a grammatical test suite for German$\rightarrow$English MT on the systems submitted at WMT19, with a detailed analysis for 107 phenomena organized in 14 categories.

Machine Translation Translation

Sentence-level quality estimation by predicting HTER as a multi-component metric

no code implementations WS 2017 Eleftherios Avramidis

This submission investigates alternative machine learning models for predicting the HTER score on the sentence level.

BIG-bench Machine Learning

The taraX\"U corpus of human-annotated machine translations

no code implementations LREC 2014 Eleftherios Avramidis, Aljoscha Burchardt, Sabine Hunsicker, Maja Popovi{\'c}, Cindy Tscherwinka, David Vilar, Hans Uszkoreit

Human translators are the key to evaluating machine translation (MT) quality and also to addressing the so far unanswered question when and how to use MT in professional translation workflows.

General Classification Machine Translation +1

The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation

no code implementations LREC 2012 Christian Federmann, Eleftherios Avramidis, Marta R. Costa-juss{\`a}, Josef van Genabith, Maite Melero, Pavel Pecina

We describe the “Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation” (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT).

Language Modelling Machine Translation +1

A Richly Annotated, Multilingual Parallel Corpus for Hybrid Machine Translation

no code implementations LREC 2012 Eleftherios Avramidis, Marta R. Costa-juss{\`a}, Christian Federmann, Josef van Genabith, Maite Melero, Pavel Pecina

This corpus aims to serve as a basic resource for further research on whether hybrid machine translation algorithms and system combination techniques can benefit from additional (linguistically motivated, decoding, and runtime) information provided by the different systems involved.

Machine Translation Translation

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