scholarly journals Free/Open-Source Resources in the Apertium Platform for Machine Translation Research and Development

2010 ◽  
Vol 93 (1) ◽  
pp. 67-76 ◽  
Author(s):  
Francis Tyers ◽  
Felipe Sánchez-Martínez ◽  
Sergio Ortiz-Rojas ◽  
Mikel Forcada
Keyword(s):  
Research And Development ◽  
Open Source ◽  
Machine Translation ◽  
Morphological Analysis ◽  
Translation Research ◽  
Part Of Speech ◽  
Open Source Framework ◽  
Finite State ◽  
Translation Systems ◽  
Free Open Source

Free/Open-Source Resources in the Apertium Platform for Machine Translation Research and DevelopmentThis paper describes the resources available in the Apertium platform, a free/open-source framework for creating rule-based machine translation systems. Resources within the platform take the form of finite-state morphologies for morphological analysis and generation, bilingual transfer lexica, probabilistic part-of-speech taggers and transfer rule files, all in standardised formats. These resources are described and some examples are given of their reuse and recycling in combination with other machine translation systems.

scholarly journals Bridging the “gApp”: improving neural machine translation systems for multiword expression detection

2020 ◽  
Vol 11 (1) ◽  
pp. 61-80
Author(s):  
Carlos Manuel Hidalgo-Ternero ◽  
Gloria Corpas Pastor
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Automatic Identification ◽  
Neural Machine Translation ◽  
Multiword Expressions ◽  
Continuous Form ◽  
Text Preprocessing ◽  
Translation Systems ◽  
Free Open Source

AbstractThe present research introduces the tool gApp, a Python-based text preprocessing system for the automatic identification and conversion of discontinuous multiword expressions (MWEs) into their continuous form in order to enhance neural machine translation (NMT). To this end, an experiment with semi-fixed verb–noun idiomatic combinations (VNICs) will be carried out in order to evaluate to what extent gApp can optimise the performance of the two main free open-source NMT systems —Google Translate and DeepL— under the challenge of MWE discontinuity in the Spanish into English directionality. In the light of our promising results, the study concludes with suggestions on how to further optimise MWE-aware NMT systems.

scholarly journals Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

2021 ◽  
Author(s):  
Tanmai Khanna ◽  
Jonathan N. Washington ◽  
Francis M. Tyers ◽  
Sevilay Bayatlı ◽  
Daniel G. Swanson ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Lexical Selection ◽  
Rule Based ◽  
Low Resource ◽  
Language Technology ◽  
Language Data ◽  
Recursive Structures ◽  
Platform Translation ◽  
Free Open Source

AbstractThis paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.

Apertium: a free/open-source platform for rule-based machine translation

2011 ◽  
Vol 25 (2) ◽  
pp. 127-144 ◽  
Author(s):  
Mikel L. Forcada ◽  
Mireia Ginestí-Rosell ◽  
Jacob Nordfalk ◽  
Jim O’Regan ◽  
Sergio Ortiz-Rojas ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Rule Based ◽  
Free Open Source

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 MorphoBr: an open source large-coverage full-form lexicon for morphological analysis of Portuguese

2018 ◽  
Vol 11 (3) ◽  
pp. 1-25
Author(s):  
Leonel Figueiredo de Alencar ◽  
Bruno Cuconato ◽  
Alexandre Rademaker
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Open Source ◽  
Computational Linguistics ◽  
Language Processing ◽  
Morphological Analysis ◽  
Computational Techniques ◽  
Processing Technologies ◽  
Finite State ◽  
Full Form

ABSTRACT: One of the prerequisites for many natural language processing technologies is the availability of large lexical resources. This paper reports on MorphoBr, an ongoing project aiming at building a comprehensive full-form lexicon for morphological analysis of Portuguese. A first version of the resource is already freely available online under an open source, free software license. MorphoBr combines analogous free resources, correcting several thousand errors and gaps, and systematically adding new entries. In comparison to the integrated resources, lexical entries in MorphoBr follow a more user-friendly format, which can be straightforwardly compiled into finite-state transducers for morphological analysis, e.g. in the context of syntactic parsing with a grammar in the LFG formalism using the XLE system. MorphoBr results from a combination of computational techniques. Errors and the more obvious gaps in the integrated resources were automatically corrected with scripts. However, MorphoBr's main contribution is the expansion in the inventory of nouns and adjectives. This was carried out by systematically modeling diminutive formation in the paradigm of finite-state morphology. This allowed MorphoBr to significantly outperform analogous resources in the coverage of diminutives. The first evaluation results show MorphoBr to be a promising initiative which will directly contribute to the development of more robust natural language processing tools and applications which depend on wide-coverage morphological analysis.KEYWORDS: computational linguistics; natural language processing; morphological analysis; full-form lexicon; diminutive formation. RESUMO: Um dos pré-requisitos para muitas tecnologias de processamento de linguagem natural é a disponibilidade de vastos recursos lexicais. Este artigo trata do MorphoBr, um projeto em desenvolvimento voltado para a construção de um léxico de formas plenas abrangente para a análise morfológica do português. Uma primeira versão do recurso já está disponível gratuitamente on-line sob uma licença de software livre e de código aberto. MorphoBr combina recursos livres análogos, corrigindo vários milhares de erros e lacunas. Em comparação com os recursos integrados, as entradas lexicais do MorphoBr seguem um formato mais amigável, o qual pode ser compilado diretamente em transdutores de estados finitos para análise morfológica, por exemplo, no contexto do parsing sintático com uma gramática no formalismo da LFG usando o sistema XLE. MorphoBr resulta de uma combinação de técnicas computacionais. Erros e lacunas mais óbvias nos recursos integrados foram automaticamente corrigidos com scripts. No entanto, a principal contribuição de MorphoBr é a expansão no inventário de substantivos e adjetivos. Isso foi alcançado pela modelação sistemática da formação de diminutivos no paradigma da morfologia de estados finitos. Isso possibilitou a MorphoBr superar de forma significativa recursos análogos na cobertura de diminutivos. Os primeiros resultados de avaliação mostram que o MorphoBr constitui uma iniciativa promissora que contribuirá de forma direta para conferir robustez a ferramentas e aplicações de processamento de linguagem natural que dependem de análise morfológica de ampla cobertura.PALAVRAS-CHAVE: linguística computacional; processamento de linguagem natural; análise morfológica; léxico de formas plenas; formação de diminutivos.

scholarly journals Sublemma-Based Neural Machine Translation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Thien Nguyen ◽  
Huu Nguyen ◽  
Phuoc Tran
Keyword(s):  
Machine Translation ◽  
Quality Data ◽  
Human Judgment ◽  
Linguistic Features ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Part Of Speech ◽  
Proposed Model ◽  
Translation Systems ◽  
Ted Talks

Powerful deep learning approach frees us from feature engineering in many artificial intelligence tasks. The approach is able to extract efficient representations from the input data, if the data are large enough. Unfortunately, it is not always possible to collect large and quality data. For tasks in low-resource contexts, such as the Russian ⟶ Vietnamese machine translation, insights into the data can compensate for their humble size. In this study of modelling Russian ⟶ Vietnamese translation, we leverage the input Russian words by decomposing them into not only features but also subfeatures. First, we break down a Russian word into a set of linguistic features: part-of-speech, morphology, dependency labels, and lemma. Second, the lemma feature is further divided into subfeatures labelled with tags corresponding to their positions in the lemma. Being consistent with the source side, Vietnamese target sentences are represented as sequences of subtokens. Sublemma-based neural machine translation proves itself in our experiments on Russian-Vietnamese bilingual data collected from TED talks. Experiment results reveal that the proposed model outperforms the best available Russian  ⟶  Vietnamese model by 0.97 BLEU. In addition, automatic machine judgment on the experiment results is verified by human judgment. The proposed sublemma-based model provides an alternative to existing models when we build translation systems from an inflectionally rich language, such as Russian, Czech, or Bulgarian, in low-resource contexts.

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