scholarly journals Applying Automatic Translation for Optical Music Recognition’s Encoding Step

2021 ◽  
Vol 11 (9) ◽  
pp. 3890
Author(s):  
Antonio Ríos-Vila ◽  
Miquel Esplà-Gomis ◽  
David Rizo ◽  
Pedro J. Ponce de León ◽  
José M. Iñesta
Keyword(s):  
Machine Translation ◽  
Research Field ◽  
Neural Systems ◽  
File Format ◽  
Automatic Translation ◽  
Recognition Phase ◽  
Music Recognition ◽  
Translation Techniques ◽  
Standard File Format ◽  
Standard Semantic

Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or discussed, and which is relevant to obtaining a standard digital score from the recognition process: the step of encoding data into a standard file format. In this paper, we address this task by proposing and evaluating the feasibility of using machine translation techniques, using statistical approaches and neural systems, to automatically convert the results of graphical encoding recognition into a standard semantic format, which can be exported as a digital score. We also discuss the implications, challenges and details to be taken into account when applying machine translation techniques to music languages, which are very different from natural human languages. This needs to be addressed prior to performing experiments and has not been reported in previous works. We also describe and detail experimental results, and conclude that applying machine translation techniques is a suitable solution for this task, as they have proven to obtain robust results.

An Improved English-to-Mizo Neural Machine Translation

2021 ◽  
Vol 20 (4) ◽  
pp. 1-21
Author(s):  
Candy Lalrempuii ◽  
Badal Soni ◽  
Partha Pakray
Keyword(s):  
Machine Translation ◽  
Model Performance ◽  
Sentence Length ◽  
Prediction Errors ◽  
Indian Languages ◽  
Neural Machine Translation ◽  
Automatic Translation ◽  
Translation Techniques ◽  
Language Pair

Machine Translation is an effort to bridge language barriers and misinterpretations, making communication more convenient through the automatic translation of languages. The quality of translations produced by corpus-based approaches predominantly depends on the availability of a large parallel corpus. Although machine translation of many Indian languages has progressively gained attention, there is very limited research on machine translation and the challenges of using various machine translation techniques for a low-resource language such as Mizo. In this article, we have implemented and compared statistical-based approaches with modern neural-based approaches for the English–Mizo language pair. We have experimented with different tokenization methods, architectures, and configurations. The performance of translations predicted by the trained models has been evaluated using automatic and human evaluation measures. Furthermore, we have analyzed the prediction errors of the models and the quality of predictions based on variations in sentence length and compared the model performance with the existing baselines.

A Survey on Document-level Neural Machine Translation

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Sameen Maruf ◽  
Fahimeh Saleh ◽  
Gholamreza Haffari
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Research Field ◽  
Translation Process ◽  
Future Directions ◽  
Translation Quality ◽  
Current State ◽  
Evaluation Strategies ◽  
Almost All ◽  
Document Level

Machine translation (MT) is an important task in natural language processing (NLP), as it automates the translation process and reduces the reliance on human translators. With the resurgence of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques for most language-pairs. Up until a few years ago, almost all of the neural translation models translated sentences independently , without incorporating the wider document-context and inter-dependencies among the sentences. The aim of this survey article is to highlight the major works that have been undertaken in the space of document-level machine translation after the neural revolution, so researchers can recognize the current state and future directions of this field. We provide an organization of the literature based on novelties in modelling and architectures as well as training and decoding strategies. In addition, we cover evaluation strategies that have been introduced to account for the improvements in document MT, including automatic metrics and discourse-targeted test sets. We conclude by presenting possible avenues for future exploration in this research field.

scholarly journals Post-editing and legal translation

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Fiorenza Mileto
Keyword(s):  
Experiential Learning ◽  
Machine Translation ◽  
Ad Hoc ◽  
Project Based Learning ◽  
Automatic Verification ◽  
Screen Readers ◽  
Automatic Translation ◽  
Legal Translation ◽  
Interdisciplinary Nature ◽  
The Creation

At UNINT, the courses dedicated to technologies are inspired by the principles of PBL (project-based learning) and experiential learning. Following this approach, in the courses dedicated to assisted and automatic translation the students perform experiments to test some aspects or address problems that are detected through the observation of the translation industry: i.e., the compatibility of screen readers with CATs for blind users, the testing of Adaptive Machine Translation (AMT) systems being developed, the verification of the usefulness of the output of Machine Translation (MT) not only for translators but also for interpreters. This year, during the automatic translation and post-editing laboratory, thanks to the interdisciplinary nature of the courses dealing with translation technologies, a group of students carried out experiments on materials made available by the teacher of active legal translation module. The aim was to verify how effective the automatic translation integrated with the assisted translation from Italian into English was on a determined type of text, using procedures like pre-editing, the creation of ad hoc translation memories based on legacy material and the automatic verification of terminology through the creation of specific glossaries.

An overview of the phrase-based statistical machine translation techniques

2012 ◽  
Vol 27 (4) ◽  
pp. 413-431 ◽  
Author(s):  
Marta Ruiz Costa-jussà
Keyword(s):  
Machine Translation ◽  
Domain Adaptation ◽  
Statistical Machine Translation ◽  
Scientific Field ◽  
Quality Improvements ◽  
General Overview ◽  
Translation Techniques ◽  
Statistical Criteria

AbstractThis work provides a general overview of the statistical machine translation (SMT) scientific field, which is a subfield of machine translation (MT). Specifically, this paper focuses on one of the most popular SMT approaches, that is, the phrase-based system.The phrase-based translation units are typically extracted using statistical criteria, and they are weighted using different models. These models are log-linearly combined in the decoding, which is in charge of choosing the most probable translation. Significant quality improvements have been produced from original phrase-based SMT systems. Among others, the main challenges are reordering, domain adaptation and evaluation.

scholarly journals MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation

2019 ◽  
Vol 28 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Sainik Kumar Mahata ◽  
Dipankar Das ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Target Language ◽  
Data Sets ◽  
Shared Task ◽  
Automatic Translation ◽  
External Data ◽  
Statistical Mt

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.

Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design

2013 ◽  
pp. 192-213
Author(s):  
Emmanuel Buabin
Keyword(s):  
Recommender System ◽  
Document Classification ◽  
Research Field ◽  
Neural Systems ◽  
Fully Integrated ◽  
Text Document ◽  
Unit Design ◽  
Boosting Algorithms ◽  
New Research ◽  
Text Document Classification

The objective is intelligent recommender system classification unit design using hybrid neural techniques. In particular, a neuroscience-based hybrid neural by Buabin (2011a) is introduced, explained, and examined for its potential in real world text document classification on the modapte version of the Reuters news text corpus. The so described neuroscience model (termed Hy-RNC) is fully integrated with a novel boosting algorithm to augment text document classification purposes. Hy-RNC outperforms existing works and opens up an entirely new research field in the area of machine learning. The main contribution of this book chapter is the provision of a step-by-step approach to modeling the hybrid system using underlying concepts such as boosting algorithms, recurrent neural networks, and hybrid neural systems. Results attained in the experiments show impressive performance by the hybrid neural classifier even with a minimal number of neurons in constituting structures.

scholarly journals A Survey on Hybrid Machine Translation

2020 ◽  
Vol 184 ◽  
pp. 01061
Author(s):  
Anusha Anugu ◽  
Gajula Ramesh
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Literature Survey ◽  
Neural Machine Translation ◽  
Translation Tools ◽  
Translation Techniques ◽  
Hybrid Machine ◽  
Hybrid Machine Translation ◽  
Translation Systems ◽  
Evaluation Techniques

Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine translation along with “Neural Machine Translation (NMT)” is summarized in this paper. Researchers have previously done lots of work on Machine Translation techniques and their evaluation techniques. Thus, we want to demonstrate an analysis of the existing techniques for machine translation including Neural Machine translation, their differences and the translation tools associated with them. Now-a-days the combination of two Machine Translation systems has the full advantage of using features from both the systems which attracts in the domain of natural language processing. So, the paper also includes the literature survey of the Hybrid Machine Translation (HMT).

scholarly journals Special Issue on Advances in Deep Learning

2020 ◽  
Vol 10 (9) ◽  
pp. 3172
Author(s):  
Diego Gragnaniello ◽  
Andrea Bottino ◽  
Sandro Cumani ◽  
Wonjoon Kim
Keyword(s):  
Deep Learning ◽  
Text Analysis ◽  
Autonomous Driving ◽  
Research Field ◽  
Media Content ◽  
Special Issue ◽  
Automatic Indexing ◽  
Automatic Translation ◽  
Indexing And Retrieval ◽  
Tremendous Impact

Nowadays, deep learning is the fastest growing research field in machine learning and has a tremendous impact on a plethora of daily life applications, ranging from security and surveillance to autonomous driving, automatic indexing and retrieval of media content, text analysis, speech recognition, automatic translation, and many others [...]

scholarly journals Apresentando o copa-trad versão 2.0 um sistema com base em corpus paralelo para pesquisa, ensino e prática da tradução

2020 ◽  
Vol 73 (1) ◽  
pp. 297-316
Author(s):  
Carlos Eduardo Silva ◽  
Lincoln Fernandes
Keyword(s):  
Machine Translation ◽  
Literary Texts ◽  
The Internet ◽  
Translation Research ◽  
Parts Of Speech ◽  
Automatic Translation ◽  
Pos Tagging ◽  
Legal Texts ◽  
Version 2.0 ◽  
Translation Systems

This paper describes COPA-TRAD Version 2.0, a parallel corpus-based system developed at the Universidade Federal de Santa Catarina (UFSC) for translation research, teaching and practice. COPA-TRAD enables the user to investigate the practices of professional translators by identifying translational patterns related to a particular element or linguistic pattern. In addition, the system allows for the comparison between human translation and automatic translation provided by three well-known machine translation systems available on the Internet (Google Translate, Microsoft Translator and Yandex). Currently, COPA-TRAD incorporates five subcorpora (Children's Literature, Literary Texts, Meta-Discourse in Translation, Subtitles and Legal Texts) and provides the following tools: parallel concordancer, monolingual concordancer, wordlist and a DIY Tool that enables the user to create his own parallel disposable corpus. The system also provides a POS-tagging tool interface to analyze and classify the parts of speech of a text.

scholarly journals Ensemble and Deep Learning for Language-Independent Automatic Selection of Parallel Data

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 26 ◽  
Author(s):  
Despoina Mouratidis ◽  
Katia Kermanidis
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Research Field ◽  
Attribute Selection ◽  
Parallel Corpora ◽  
Automatic Selection ◽  
Parallel Data ◽  
First Time ◽  
Learning Architectures ◽  
Selection Of

Machine translation is used in many applications in everyday life. Due to the increase of translated documents that need to be organized as useful or not (for building a translation model), the automated categorization of texts (classification), is a popular research field of machine learning. This kind of information can be quite helpful for machine translation. Our parallel corpora (English-Greek and English-Italian) are based on educational data, which are quite difficult to translate. We apply two state of the art architectures, Random Forest (RF) and Deeplearnig4j (DL4J), to our data (which constitute three translation outputs). To our knowledge, this is the first time that deep learning architectures are applied to the automatic selection of parallel data. We also propose new string-based features that seem to be effective for the classifier, and we investigate whether an attribute selection method could be used for better classification accuracy. Experimental results indicate an increase of up to 4% (compared to our previous work) using RF and rather satisfactory results using DL4J.

Sign in / Sign up

Export Citation Format

Share Document