Efficient Use of Resources for Statistical Machine Translation

2017 ◽  
Vol 37 (5) ◽  
pp. 307
Author(s):  
Karunesh Kumar Arora ◽  
Shyam Sunder Agrawal
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Noise Removal ◽  
Translation System ◽  
Test Case ◽  
Digital Collections ◽  
Training Corpus ◽  
Use Of Resources ◽  
Parallel Data ◽  
Translation Systems

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Machine translation has great potential to expand the audience for ever increasing digital collections. Success of data driven machine translation systems is governed by the volume of parallel data on which these systems are being modelled. The languages which do not have such resources in huge quantity, the optimum utilisation of them can only be assured through their quality. Morphologically rich language like Hindi poses further challenge, due to </span><span>having more number of orthographic inflections for a given word and presence of non-standard word spellings in </span><span>the corpus. This increases the chances of getting more number of words which are unseen in the training corpus. In this paper, the objective is to reduce redundancy of available corpus and utilise the other resources as well, to make best use of resources. Reduction in number of words unseen to the translation model is achieved through text noise removal, spell normalisation and utilising English WordNet (EWN). The test case presented here is for English-Hindi language pair. The results achieved are promising and set example for other morphological rich languages to optimise the resources to improve the performance of the translation system. </span></p></div></div></div>

Efficient Use of Resources for Statistical Machine Translation

2017 ◽  
Vol 37 (5) ◽  
pp. 307
Author(s):  
Karunesh Kumar Arora ◽  
Shyam Sunder Agrawal
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Noise Removal ◽  
Translation System ◽  
Test Case ◽  
Digital Collections ◽  
Training Corpus ◽  
Use Of Resources ◽  
Parallel Data ◽  
Translation Systems

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Machine translation has great potential to expand the audience for ever increasing digital collections. Success of data driven machine translation systems is governed by the volume of parallel data on which these systems are being modelled. The languages which do not have such resources in huge quantity, the optimum utilisation of them can only be assured through their quality. Morphologically rich language like Hindi poses further challenge, due to </span><span>having more number of orthographic inflections for a given word and presence of non-standard word spellings in </span><span>the corpus. This increases the chances of getting more number of words which are unseen in the training corpus. In this paper, the objective is to reduce redundancy of available corpus and utilise the other resources as well, to make best use of resources. Reduction in number of words unseen to the translation model is achieved through text noise removal, spell normalisation and utilising English WordNet (EWN). The test case presented here is for English-Hindi language pair. The results achieved are promising and set example for other morphological rich languages to optimise the resources to improve the performance of the translation system. </span></p></div></div></div>

Translation of Medical Texts using Neural Networks

2016 ◽  
Vol 5 (4) ◽  
pp. 51-66 ◽  
Author(s):  
Krzysztof Wolk ◽  
Krzysztof P. Marasek
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
European Medicines Agency ◽  
Translation System ◽  
Training Methods ◽  
Neural Machine Translation ◽  
Machine Translation System ◽  
Source Sentence ◽  
Parallel Text ◽  
Translation Systems

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.

scholarly journals Improving Machine Translation Performance by Exploiting Non-Parallel Corpora

2005 ◽  
Vol 31 (4) ◽  
pp. 477-504 ◽  
Author(s):  
Dragos Stefan Munteanu ◽  
Daniel Marcu
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpora ◽  
Parallel Corpus ◽  
Scarce Resources ◽  
Parallel Data ◽  
Machine Translation System ◽  
Novel Method ◽  
Arabic And English

We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.

scholarly journals Human evaluation of three machine translation systems: from quality to attitudes by professional translators

2021 ◽  
pp. 123-148
Author(s):  
Anna Fernández Torné ◽  
Anna Matamala
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Evaluation Process ◽  
Translation System ◽  
Neural Machine Translation ◽  
System A ◽  
Human Evaluation ◽  
Machine Translation System ◽  
Translation Systems ◽  
Document Level

This article aims to compare three machine translation systems with a focus on human evaluation. The systems under analysis are a domain-adapted statistical machine translation system, a domain-adapted neural machine translation system and a generic machine translation system. The comparison is carried out on translation from Spanish into German with industrial documentation of machine tool components and processes. The focus is on the human evaluation of the machine translation output, specifically on: fluency, adequacy and ranking at the segment level; fluency, adequacy, need for post-editing, ease of post-editing, and mental effort required in post-editing at the document level; productivity (post-editing speed and post-editing effort) and attitudes. Emphasis is placed on human factors in the evaluation process.

scholarly journals Machine Translation System for the Industry Domain and Croatian Language

2020 ◽  
Vol 44 (1) ◽  
pp. 33-50
Author(s):  
Ivan Dunđer
Keyword(s):  
Machine Translation ◽  
Computational Linguistics ◽  
Technology Development ◽  
Domain Adaptation ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Domain Specific ◽  
Extensive Evaluation ◽  
Machine Translation System ◽  
Translation Systems

Machine translation is increasingly becoming a hot research topic in information and communication sciences, computer science and computational linguistics, due to the fact that it enables communication and transferring of meaning across different languages. As the Croatian language can be considered low-resourced in terms of available services and technology, development of new domain-specific machine translation systems is important, especially due to raised interest and needs of industry, academia and everyday users. Machine translation is not perfect, but it is crucial to assure acceptable quality, which is purpose-dependent. In this research, different statistical machine translation systems were built – but one system utilized domain adaptation in particular, with the intention of boosting the output of machine translation. Afterwards, extensive evaluation has been performed – in form of applying several automatic quality metrics and human evaluation with focus on various aspects. Evaluation is done in order to assess the quality of specific machine-translated text.

scholarly journals Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

2016 ◽  
Vol 6 (3) ◽  
pp. 1059 ◽  
Author(s):  
K. Jaya ◽  
Deepa Gupta
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Research Work ◽  
Translation System ◽  
Indian Languages ◽  
Translation Quality ◽  
Parallel Data ◽  
Machine Translation System ◽  
Best Parameter

Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing  these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work  becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model.  The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language.  This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used.  With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.

scholarly journals Developing Statistical Machine Translation System for English and Nigerian Languages

2018 ◽  
pp. 1-8
Author(s):  
Ignatius Ikechukwu Ayogu ◽  
Adebayo Olusola Adetunmbi ◽  
Bolanle Adefowoke Ojokoh
Keyword(s):  
Error Analysis ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpora ◽  
Translation Tools ◽  
Global Demand ◽  
Machine Translation System ◽  
Translation Systems

The global demand for translation and translation tools currently surpasses the capacity of available solutions. Besides, there is no one-solution-fits-all, off-the-shelf solution for all languages. Thus, the need and urgency to increase the scale of research for the development of translation tools and devices continue to grow, especially for languages suffering under the pressure of globalisation. This paper discusses our experiments on translation systems between English and two Nigerian languages: Igbo and Yorùbá. The study is setup to build parallel corpora, train and experiment English-to-Igbo, (), English-to-Yorùbá, () and Igbo-to-Yorùbá, () phrase-based statistical machine translation systems. The systems were trained on parallel corpora that were created for each language pair using text from the religious domain in the course of this research. A BLEU score of 30.04, 29.01 and 18.72 respectively was recorded for the English-to-Igbo, English-to-Yorùbá and Igbo-to-Yorùbá MT systems. An error analysis of the systems’ outputs was conducted using a linguistically motivated MT error analysis approach and it showed that errors occurred mostly at the lexical, grammatical and semantic levels. While the study reveals the potentials of our corpora, it also shows that the size of the corpora is yet an issue that requires further attention. Thus an important target in the immediate future is to increase the quantity and quality of the data.  

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

2016 ◽  
Vol 6 (3) ◽  
pp. 1059 ◽  
Author(s):  
K. Jaya ◽  
Deepa Gupta
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Research Work ◽  
Translation System ◽  
Indian Languages ◽  
Translation Quality ◽  
Parallel Data ◽  
Machine Translation System ◽  
Best Parameter

Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing  these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work  becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model.  The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language.  This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used.  With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.

scholarly journals Phrase Table Induction Using In-Domain Monolingual Data for Domain Adaptation in Statistical Machine Translation

2017 ◽  
Vol 5 ◽  
pp. 487-500
Author(s):  
Benjamin Marie ◽  
Atsushi Fujita
Keyword(s):  
Machine Translation ◽  
Domain Adaptation ◽  
Statistical Machine Translation ◽  
Cartesian Product ◽  
Translation System ◽  
General Domain ◽  
Parallel Data ◽  
Machine Translation System ◽  
Baseline System ◽  
Target Languages

We present a new framework to induce an in-domain phrase table from in-domain monolingual data that can be used to adapt a general-domain statistical machine translation system to the targeted domain. Our method first compiles sets of phrases in source and target languages separately and generates candidate phrase pairs by taking the Cartesian product of the two phrase sets. It then computes inexpensive features for each candidate phrase pair and filters them using a supervised classifier in order to induce an in-domain phrase table. We experimented on the language pair English–French, both translation directions, in two domains and obtained consistently better results than a strong baseline system that uses an in-domain bilingual lexicon. We also conducted an error analysis that showed the induced phrase tables proposed useful translations, especially for words and phrases unseen in the parallel data used to train the general-domain baseline system.

Translation of Medical Texts using Neural Networks

2020 ◽  
pp. 1137-1154
Author(s):  
Krzysztof Wolk ◽  
Krzysztof P. Marasek
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
European Medicines Agency ◽  
Translation System ◽  
Training Methods ◽  
Neural Machine Translation ◽  
Machine Translation System ◽  
Source Sentence ◽  
Parallel Text ◽  
Translation Systems

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.

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