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

Machine translation evaluation resources and methods: a survey

We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT. This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content

BILINGUAL MULTIMODAL SYSTEM FOR TEXT-TO-AUDIOVISUAL SPEECH AND SIGN LANGUAGE SYNTHESIS

We present a conceptual model, architecture and software of a multimodal system for audio-visual speech and sign language synthesis by the input text. The main components of the developed multimodal synthesis system (signing avatar) are: automatic text processor for input text analysis; simulation 3D model of human's head; computer text-to-speech synthesizer; a system for audio-visual speech synthesis; simulation 3D model of human’s hands and upper body; multimodal user interface integrating all the components for generation of audio, visual and signed speech. The proposed system performs automatic translation of input textual information into speech (audio information) and gestures (video information), information fusion and its output in the form of multimedia information. A user can input any grammatically correct text in Russian or Czech languages to the system; it is analyzed by the text processor to detect sentences, words and characters. Then this textual information is converted into symbols of the sign language notation. We apply international «Hamburg Notation System» - HamNoSys, which describes the main differential features of each manual sign: hand shape, hand orientation, place and type of movement. On their basis the 3D signing avatar displays the elements of the sign language. The virtual 3D model of human’s head and upper body has been created using VRML virtual reality modeling language, and it is controlled by the software based on OpenGL graphical library. The developed multimodal synthesis system is a universal one since it is oriented for both regular users and disabled people (in particular, for the hard-of-hearing and visually impaired), and it serves for multimedia output (by audio and visual modalities) of input textual information

Incorporating source-language paraphrases into phrase-based SMT with confusion networks

To increase the model coverage, sourcelanguage paraphrases have been utilized to boost SMT system performance. Previous work showed that word lattices constructed from paraphrases are able to reduce out-ofvocabulary words and to express inputs in different ways for better translation quality. However, such a word-lattice-based method suffers from two problems: 1) path duplications in word lattices decrease the capacities for potential paraphrases; 2) lattice decoding in SMT dramatically increases the search space and results in poor time efficiency. Therefore, in this paper, we adopt word confusion networks as the input structure to carry source-language paraphrase information. Similar to previous work, we use word lattices to build word confusion networks for merging of duplicated paths and faster decoding. Experiments are carried out on small-, medium- and large-scale English– Chinese translation tasks, and we show that compared with the word-lattice-based method, the decoding time on three tasks is reduced significantly (up to 79%) while comparable translation quality is obtained on the largescale task

TectoMT – a deep-­linguistic core of the combined Chimera MT system

Chimera is a machine translation system that combines the TectoMT deep-linguistic core with phrase-based MT system Moses. For English–Czech pair it also uses the Depfix post-correction system. All the components run on Unix/Linux platform and are open source (available from Perl repository CPAN and the LINDAT/CLARIN repository). The main website is https://ufal.mff.cuni.cz/tectomt. The development is currently supported by the QTLeap 7th FP project (http://qtleap.eu)