Problèmes de traduction automatique des constructions à verbes supports

2000 ◽  
Vol 23 (2) ◽  
pp. 253-267
Author(s):  
Elisabete Ranchhod
Keyword(s):  
Machine Translation ◽  
Point Of View ◽  
Target Language ◽  
Source Language ◽  
Automatic Translation ◽  
Traduction Automatique

Summary The constructions with support verbs raise specific problems in Machine Translation. Within the scope of this note, we first characterise, from a linguistic point of view, the sentences with support verbs. That characterisation will be illustrated by examples from French and Portuguese. The difficulties in the automatic translation of support verbs constructions will be illustrated with examples from Portuguese, taken as source language, and French, taken as target language.

scholarly journals On Improper Machine Translations in Press Reports

2020 ◽  
Vol 11 (2) ◽  
pp. 330
Author(s):  
Huiqiong Duan ◽  
Xinyu Hu ◽  
Yidan Gao
Keyword(s):  
Natural Language ◽  
Machine Translation ◽  
Target Language ◽  
Cultural Meanings ◽  
Source Language ◽  
Automatic Translation ◽  
News Releases ◽  
Sentence Patterns

Machine translation, also known as automatic translation, is the process of converting one natural language (source language) into another natural language (target language) by using networks. There are some language errors in current machine translation in news releases. Having compared human translators’ translation texts and machine translation results, improper machine translation results are found. They are inaccurate use of words, rigid sentence patterns and unclear expression of specific cultural meanings. Accurate machine translation needs the assistance of human translators.

scholarly journals Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages

2017 ◽  
Vol 108 (1) ◽  
pp. 257-269 ◽  
Author(s):  
Nasser Zalmout ◽  
Nizar Habash
Keyword(s):  
Machine Translation ◽  
Performance Enhancement ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Source Language ◽  
Context Variable ◽  
Significant Performance ◽  
Morphologically Rich Languages ◽  
Target Languages ◽  
Language Text

AbstractTokenization is very helpful for Statistical Machine Translation (SMT), especially when translating from morphologically rich languages. Typically, a single tokenization scheme is applied to the entire source-language text and regardless of the target language. In this paper, we evaluate the hypothesis that SMT performance may benefit from different tokenization schemes for different words within the same text, and also for different target languages. We apply this approach to Arabic as a source language, with five target languages of varying morphological complexity: English, French, Spanish, Russian and Chinese. Our results show that different target languages indeed require different source-language schemes; and a context-variable tokenization scheme can outperform a context-constant scheme with a statistically significant performance enhancement of about 1.4 BLEU points.

scholarly journals Controlling Neural Machine Translation Formality with Synthetic Supervision

2020 ◽  
Vol 34 (05) ◽  
pp. 8568-8575
Author(s):  
Xing Niu ◽  
Marine Carpuat
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Sentence Pair ◽  
English Sentence ◽  
Neural Machine Translation ◽  
Source Language ◽  
Training Scheme ◽  
Training Examples ◽  
Language Content ◽  
Missing Element

This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience. Framing this problem as a neural sequence-to-sequence task ideally requires training triplets consisting of a bilingual sentence pair labeled with target language formality. However, in practice, available training examples are limited to English sentence pairs of different styles, and bilingual parallel sentences of unknown formality. We introduce a novel training scheme for multi-task models that automatically generates synthetic training triplets by inferring the missing element on the fly, thus enabling end-to-end training. Comprehensive automatic and human assessments show that our best model outperforms existing models by producing translations that better match desired formality levels while preserving the source meaning.1

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.

A Knowledge-Based Machine Translation Using AI Technique

2018 ◽  
Vol 6 (3) ◽  
pp. 79-92
Author(s):  
Sahar A. El-Rahman ◽  
Tarek A. El-Shishtawy ◽  
Raafat A. El-Kammar
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Translation System ◽  
Module Structure ◽  
Source Language ◽  
Word Category ◽  
Knowledge Based ◽  
Language Analysis ◽  
Transfer Rules

This article presents a realistic technique for the machine aided translation system. In this technique, the system dictionary is partitioned into a multi-module structure for fast retrieval of Arabic features of English words. Each module is accessed through an interface that includes the necessary morphological rules, which directs the search toward the proper sub-dictionary. Another factor that aids fast retrieval of Arabic features of words is the prediction of the word category, and accesses its sub-dictionary to retrieve the corresponding attributes. The system consists of three main parts, which are the source language analysis, the transfer rules between source language (English) and target language (Arabic), and the generation of the target language. The proposed system is able to translate, some negative forms, demonstrations, and conjunctions, and also adjust nouns, verbs, and adjectives according their attributes. Then, it adds the symptom of Arabic words to generate a correct sentence.

scholarly journals THEORETICAL AND PRACTICAL PECULIARITIES OF TRANSLATING CULTURE-SPECIFIC TERMS

2020 ◽  
Vol 74 (4) ◽  
pp. 494-497
Author(s):  
B. Mizamkhan ◽  
◽  
T. Kalibekuly ◽  
Keyword(s):  
Point Of View ◽  
Target Language ◽  
Mutual Understanding ◽  
Cultural Backgrounds ◽  
Source Language ◽  
Language Groups ◽  
Adequate Understanding ◽  
Cultural Connotations ◽  
Different Cultures ◽  
Cultural Linguistics

The term “culture-specific vocabulary” appeared in the 1980s. Problems of translating culture-specific terms from one language to another have always been a serious issue for translators. It causes even more problems if the languages being compared belong to different language groups and represent different cultures. Nevertheless, the study of culture-specific vocabulary helps to achieve the adequacy of translation, which in turn helps speakers of different languages ​​and cultures to achieve mutual understanding. The above emphasizes the relevance and timeliness of the study of translation from the point of view of cultural linguistics. This paper will examine the peculiarities of translating culture-specific terms from Kazakh into English. It provides different methods of translating cultural connotations, taking into account the ways of living and thinking, as well the historical and cultural backgrounds embedded in the source language (hereafter SL) and target language (hereafter TL). These methods will be analyzed using specific examples, originals and translations of such works as “The Path of Abai” by Mukhtar Auezov and “Nomads” by Ilyas Yessenberlin. Therefore, the main aim of the paper is to try to explain main approaches and theories needed for adequate understanding of different cultures through translation.

scholarly journals A Study of Neural Machine Translation from Chinese to Urdu

2020 ◽  
Vol 2 (4) ◽  
pp. 28
Author(s):  
. Zeeshan
Keyword(s):  
Machine Translation ◽  
Chinese Language ◽  
Language Translation ◽  
Target Language ◽  
Foreign Languages ◽  
Neural Machine Translation ◽  
Source Language ◽  
Great Progress ◽  
Score Method ◽  
Translation Methods

Machine Translation (MT) is used for giving a translation from a source language to a target language. Machine translation simply translates text or speech from one language to another language, but this process is not sufficient to give the perfect translation of a text due to the requirement of identification of whole expressions and their direct counterparts. Neural Machine Translation (NMT) is one of the most standard machine translation methods, which has made great progress in the recent years especially in non-universal languages. However, local language translation software for other foreign languages is limited and needs improving. In this paper, the Chinese language is translated to the Urdu language with the help of Open Neural Machine Translation (OpenNMT) in Deep Learning. Firstly, a Chineseto Urdu language sentences datasets were established and supported with Seven million sentences. After that, these datasets were trained by using the Open Neural Machine Translation (OpenNMT) method. At the final stage, the translation was compared to the desired translation with the help of the Bleu Score Method.

scholarly journals Query Expansion for Slovak to Bulgarian Language Machine Translation using Parallel Search

2021 ◽  
pp. 351-357
Author(s):  
VELISLAVA STOYKOVA ◽  
DANIELA MAJCHRAKOVA
Keyword(s):  
Machine Translation ◽  
Query Expansion ◽  
Statistical Approach ◽  
Semantic Relations ◽  
Target Language ◽  
Parallel Search ◽  
Keyword Query ◽  
Source Language ◽  
Standard Presentation ◽  
Standard Semantic

The paper presents results of the application of a statistical approach for Slovak to Bulgarian language machine translation. It uses Information Retrieval inspired search techniques and employs sever alalgorithmic steps of parallel statistical search with query expansion in Slovak-Bulgarian EUROPARL 7 Corpus using the Sketch Engine software and its scoring. The search includes the generation of concordances,collocations, word sketch differences, word sketches, and thesauri of the studied keyword (query) by using a statistical scoring, which is regarded as intermediate (inter-lingual) semantic standard presentation by means of which the studied keyword (from the source language) is mapped together with its possible translation equivalents (onto the target language. The results present the study of adjectival collocabillity in both Slovak and Bulgarian language from the corpus of political speech texts outlining the standard semantic relations based on the evaluation of statistical scoring. Finally, the advantages and shortcomings of the approach are discussed.

scholarly journals Knowledge Graphs Effectiveness in Neural Machine Translation Improvement

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Benyamin Ahmadnia ◽  
Bonnie J. Dorr ◽  
Parisa Kordjamshidi
Keyword(s):  
Machine Translation ◽  
Semantic Representation ◽  
Language Translation ◽  
Semantic Relations ◽  
Training Data ◽  
Target Language ◽  
Neural Machine Translation ◽  
Source Language ◽  
Knowledge Graphs ◽  
Unknown Words

Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two embedding constraints, both of which employ Entity Linking (EL)---assigning a unique identity to entities---to associate words in training sentences with those in the KG: (1) a monolingual embedding constraint that supports an enhanced semantic representation of the source words through access to relations between entities in a KG; and (2) a bilingual embedding constraint that forces entity relations in the source-language to be carried over to the corresponding entities in the target-language translation. The method is evaluated for English-Spanish translation exploiting Freebase as a source of knowledge. Our experimental results show that exploiting KG information not only decreases the number of unknown words in the translation but also improves translation quality.

An Experimental Platform for Cross-Language Document Retrieval

2013 ◽  
Vol 284-287 ◽  
pp. 3325-3329
Author(s):  
Long Yue Wang ◽  
Derek F. Wong ◽  
Lidia S. Chao
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Document Retrieval ◽  
Training Data ◽  
Target Language ◽  
Source Language ◽  
Experimental Platform ◽  
Precision Evaluation ◽  
Query Generation ◽  
Cross Language

This paper presents a proposed Cross-Language Document Retrieval experimental platform integrated with preprocessing of training data, document translation, query generation, document retrieval and precision evaluation modules. Given a certain document in source language, it will be translated into target language by statistical machine translation module which is trained by selected training data. The query generation module then selects the most relevant words in the translated version of the document as searching query. After all the documents in the target language are ranked by the document retrieval module, the system will choose the N-best documents as its target language versions. Finally, the results can be evaluated by precision evaluator, which can reflect the merits of the strategies. Experimental results showed that this platform was effective and achieved very good performance.

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