Combining data-driven MT systems for improved sign language translation

In this paper, we investigate the feasibility of combining two data-driven machine translation (MT) systems for the translation of sign languages (SLs). We take the MT systems of two prominent data-driven research groups, the MaTrEx system developed at DCU and the Statistical Machine Translation (SMT) system developed at RWTH Aachen University, and apply their respective approaches to the task of translating Irish Sign Language and German Sign Language into English and German. In a set of experiments supported by automatic evaluation results, we show that there is a definite value to the prospective merging of MaTrEx’s Example-Based MT chunks and distortion limit increase with RWTH’s constraint reordering

The ATIS sign language corpus

Systems that automatically process sign language rely on appropriate data. We therefore present the ATIS sign language corpus that is based on the domain of air travel information. It is available for five languages, English, German, Irish sign language, German sign language and South African sign language. The corpus can be used for different tasks like automatic statistical translation and automatic sign language recognition and it allows the specific modelling of spatial references in signing space

Morbus Parkinson: Investigation of the aggregation potential of α-Synuclein and of the inhibition microglia-mediated neuroinflammation

Die Parkinson-Erkrankung ist eine schwerwiegende, kognitive und neuromotorische Dysfunktion, welche sowohl durch genetische Ursachen, als auch durch Umwelteinflüsse hervorgerufen werden kann. Eine besondere Bedeutung kommt dabei dem Protein α-Synuklein zu, welches zu Oligomeren aggregieren kann. Das aggregierte α-Synuklein lagert sich dann im neuralen Gewebe ab und entfaltet dort eine neurodegenerative Wirkung, unter anderem durch zytotoxische Effekte auf die umgebenden neuronalen Zellen. Neben der kanonischen Variante von α-Synuklein (SNCA140) können durch alternatives Spleißen auch weitere, verkürzte Isoformen vorliegen (SNCA126, SNCA112 und SNCA98), ob diese jedoch ein anderes Aggregationsverhalten als die kanonische Variante zeigen, war bisher nicht bekannt. Im Rahmen dieser Arbeit konnte dabei gezeigt werden, dass diese Isoformen nicht nur ein unterschiedliches, wenn auch im Vergleich zur kanonischen Form stets deutlich niedrigeres Expressionsniveau in verschiedenen neuronalen Geweben zeigen, sondern auch unterschiedlich aggregieren. Im Zusammenhang mit verschiedenen Therapien zur Behandlung der Parkinson-Erkrankung wird die Verwendung verschiedener Naturstoffe, welchen eine antiinflammatorische Wirkung zugeschrieben wird, diskutiert. Im Rahmen dieser Arbeit wurde daher die putative, antiinflammatorische und somit neuroprotektive Wirkung der drei Naturstoffe Curcumin, Coumestrol und Sulforaphan auf mikrogliale Zellen untersucht.Parkinson’s disease is a severe cognitive and neuromotoric dysfunction that can be induced by genetic mutations or other environmental factors. Thereby, the protein α-Synuclein plays a central role in the Parkinson’s disease, as this protein can aggregate to form oligomeric structures, which precipitate within the neural tissue. There, these aggregates perform a neurodegenerative action by the induction of cytotoxic effects on neighboring neural cells. Besides the canonical α-Synuclein isoform (SNCA140), alternated splicing can result in the generation of shortened isoforms (SNCA126, SNCA112 und SNCA98). As differences in the formation of oligomeric structures have not been studied so far, this was addressed in this thesis. We were able to show that the different isoforms not only show different gene expression levels in different neural tissues, but also differ in their aggregation potential, if compared to the canonical isoform (SNCA140). In context of putative therapeutic strategies to treat the Parkinson’s disease, the use of different natural compounds that show anti-inflammatory effects is also discussed. Here, we were able to demonstrate the anti-inflammatory and thus neuroprotective effect of the three compounds coumestrol, curcumin and sulforaphane towards microglial cells

TAN Classifiers Based on Decomposable Distributions

The original publication is available at www.springerlink.comIn this paper we present several Bayesian algorithms for learning Tree Augmented Naive Bayes (TAN) models. We extend the results in Meila & Jaakkola (2000a) to TANs by proving that accepting a prior decomposable distribution over TAN's, we can compute the exact Bayesian model averaging over TAN structures and parameters in polynomial time. Furthermore, we prove that the k-maximum a posteriori (MAP) TAN structures can also be computed in polynomial time. We use these results to correct minor errors in Meila & Jaakkola (2000a) and to construct several TAN based classifiers provide consistently better predictions over Irvine datasets and artificially generated data than TAN based classifiers proposed in the literature.Peer reviewe

Automatic Categorization for Improving Spanish into Spanish Sign Language Machine Translation

This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Driver's License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82

Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set