A novel rule based machine translation scheme from Greek to Greek Sign Language: Production of different types of large corpora and Language Models evaluation

2018 ◽  
Vol 51 ◽  
pp. 110-135 ◽  
Author(s):  
Dimitrios Kouremenos ◽  
Klimis Ntalianis ◽  
Stefanos Kollias
Keyword(s):  
Sign Language ◽  
Machine Translation ◽  
Language Production ◽  
Language Models ◽  
Rule Based ◽  
Different Types ◽  
Translation Scheme

scholarly journals Text2Sign: Towards Sign Language Production Using Neural Machine Translation and Generative Adversarial Networks

2020 ◽  
Vol 128 (4) ◽  
pp. 891-908 ◽  
Author(s):  
Stephanie Stoll ◽  
Necati Cihan Camgoz ◽  
Simon Hadfield ◽  
Richard Bowden
Keyword(s):  
Sign Language ◽  
Machine Translation ◽  
Language Production ◽  
Language Translation ◽  
Spoken Language ◽  
Generative Adversarial Networks ◽  
Motion Generation ◽  
High Definition ◽  
Neural Machine Translation ◽  
Adversarial Networks

AbstractWe present a novel approach to automatic Sign Language Production using recent developments in Neural Machine Translation (NMT), Generative Adversarial Networks, and motion generation. Our system is capable of producing sign videos from spoken language sentences. Contrary to current approaches that are dependent on heavily annotated data, our approach requires minimal gloss and skeletal level annotations for training. We achieve this by breaking down the task into dedicated sub-processes. We first translate spoken language sentences into sign pose sequences by combining an NMT network with a Motion Graph. The resulting pose information is then used to condition a generative model that produces photo realistic sign language video sequences. This is the first approach to continuous sign video generation that does not use a classical graphical avatar. We evaluate the translation abilities of our approach on the PHOENIX14T Sign Language Translation dataset. We set a baseline for text-to-gloss translation, reporting a BLEU-4 score of 16.34/15.26 on dev/test sets. We further demonstrate the video generation capabilities of our approach for both multi-signer and high-definition settings qualitatively and quantitatively using broadcast quality assessment metrics.

scholarly journals A Translation System That Converts English Text to American Sign Language Enhanced with Deep Learning Modules

2019 ◽  
Vol 8 (12) ◽  
pp. 5378-5383
Keyword(s):  
Deep Learning ◽  
American Sign Language ◽  
Sign Language ◽  
Machine Translation ◽  
Language Processing ◽  
Rapid Development ◽  
English Text ◽  
Translation System ◽  
American Sign ◽  
Rule Based

A recent surge in interest to create translation systems inclusive of sign languages is engendered by not only the rapid development of various approaches in the field of machine translation, but also the increased awareness of the struggles of the deaf community to comprehend written English. This paper describes the working of SILANT (SIgn LANguage Translator), a machine translation system that converts English to American Sign Language (ASL) using the principles of Natural Language Processing (NLP) and Deep Learning. The translation of English text is based on transformational rules which generates an intermediate representation which in turn spawns appropriate ASL animations. Although this kind of rule-based translation is notorious for being an accurate yet narrow approach, in this system, we broaden the scope of the translation using a synonym network and paraphrasing module which implements deep learning algorithms. In doing so, we are able to achieve both the accuracy of a rule-based approach and the scale of a deep learning one.

Proper Name Machine Translation from Japanese to Japanese Sign Language

2014 ◽  
Author(s):  
Taro Miyazaki ◽  
Naoto Kato ◽  
Seiki Inoue ◽  
Shuichi Umeda ◽  
Makiko Azuma ◽  
...  
Keyword(s):  
Sign Language ◽  
Machine Translation ◽  
Proper Name ◽  
Japanese Sign Language

scholarly journals Development of Speech Recognition Systems in Emergency Call Centers

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 634
Author(s):  
Alakbar Valizada ◽  
Natavan Akhundova ◽  
Samir Rustamov
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov ◽  
Call Centers ◽  
The Other ◽  
Language Models ◽  
Emergency Call ◽  
Acoustic Model ◽  
Different Types ◽  
Recognition Systems

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.

scholarly journals STOUT: SMILES to IUPAC names using neural machine translation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Machine Translation ◽  
Similarity Index ◽  
Chemical Compounds ◽  
Human Beings ◽  
International Union ◽  
Rule Based ◽  
Neural Machine Translation ◽  
Tanimoto Similarity ◽  
Reverse Translation ◽  
String Representation

AbstractChemical compounds can be identified through a graphical depiction, a suitable string representation, or a chemical name. A universally accepted naming scheme for chemistry was established by the International Union of Pure and Applied Chemistry (IUPAC) based on a set of rules. Due to the complexity of this ruleset a correct chemical name assignment remains challenging for human beings and there are only a few rule-based cheminformatics toolkits available that support this task in an automated manner. Here we present STOUT (SMILES-TO-IUPAC-name translator), a deep-learning neural machine translation approach to generate the IUPAC name for a given molecule from its SMILES string as well as the reverse translation, i.e. predicting the SMILES string from the IUPAC name. In both cases, the system is able to predict with an average BLEU score of about 90% and a Tanimoto similarity index of more than 0.9. Also incorrect predictions show a remarkable similarity between true and predicted compounds.

scholarly journals Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System

2018 ◽  
Vol 28 (09) ◽  
pp. 1850007
Author(s):  
Francisco Zamora-Martinez ◽  
Maria Jose Castro-Bleda
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Traditional Approach ◽  
Computational Cost ◽  
Integrated Approach ◽  
Language Models ◽  
Translation System ◽  
Neural Net ◽  
Network Language

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

Methods for integrating rule-based and statistical systems for Arabic to English machine translation

2011 ◽  
Vol 26 (1-2) ◽  
pp. 67-83 ◽  
Author(s):  
Rabih Zbib ◽  
Michael Kayser ◽  
Spyros Matsoukas ◽  
John Makhoul ◽  
Hazem Nader ◽  
...  
Keyword(s):  
Machine Translation ◽  
Rule Based ◽  
Statistical Systems

scholarly journals Adabot: Fault-Tolerant Java Decompiler (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13861-13862
Author(s):  
Zhiming Li ◽  
Qing Wu ◽  
Kun Qian
Keyword(s):  
Reverse Engineering ◽  
Machine Translation ◽  
Fault Tolerant ◽  
Safety Concern ◽  
Statistical Machine Translation ◽  
Abstract Syntax ◽  
Rule Based ◽  
Abstract Syntax Tree ◽  
Important Field ◽  
Internal Architecture

Reverse Engineering has been an extremely important field in software engineering, it helps us to better understand and analyze the internal architecture and interrealtions of executables. Classical Java reverse engineering task includes disassembly and decompilation. Traditional Abstract Syntax Tree (AST) based disassemblers and decompilers are strictly rule defined and thus highly fault intolerant when bytecode obfuscation were introduced for safety concern. In this work, we view decompilation as a statistical machine translation task and propose a decompilation framework which is fully based on self-attention mechanism. Through better adaption to the linguistic uniqueness of bytecode, our model fully outperforms rule-based models and previous works based on recurrence mechanism.

scholarly journals Rule Based Case Transfer in Tamil-Malayalam Machine Translation

2014 ◽  
Vol 84 (1) ◽  
pp. 41-52
Author(s):  
S. Lakshmi ◽  
Sobha Lalitha Devi
Keyword(s):  
Machine Translation ◽  
Rule Based
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