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Found 27 papers, 3 papers with code
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples
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.
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output
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.
Using Neural Machine Translation Methods for Sign Language Translation
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.
Linguistic Evaluation for the 2021 State-of-the-art Machine Translation Systems for German to English and English to German
We are using a semi-automated test suite in order to provide a fine-grained linguistic evaluation for state-of-the-art machine translation systems.
Automatic generation of a 3D sign language avatar on AR glasses given 2D videos of human signers
In this paper we present a prototypical implementation of a pipeline that allows the automatic generation of a German Sign Language avatar from 2D video material.
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena
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.
Fine-grained linguistic evaluation for state-of-the-art Machine Translation
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).
Fine-grained evaluation of German-English Machine Translation based on a Test Suite
We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite.
Linguistic evaluation of German-English Machine Translation using a Test Suite
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.
Train, Sort, Explain: Learning to Diagnose Translation Models
Evaluating translation models is a trade-off between effort and detail.
Sentence-level quality estimation by predicting HTER as a multi-component metric
This submission investigates alternative machine learning models for predicting the HTER score on the sentence level.
Tools and Guidelines for Principled Machine Translation Development
This work addresses the need to aid Machine Translation (MT) development cycles with a complete workflow of MT evaluation methods.
The taraX\"U corpus of human-annotated machine translations
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.
The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation
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).
Involving Language Professionals in the Evaluation of Machine Translation
Significant breakthroughs in machine translation only seem possible if human translators are taken into the loop.
A Richly Annotated, Multilingual Parallel Corpus for Hybrid Machine Translation
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.
