Automatic categorization for improving Spanish into Spanish Sign Language machine translation

López-Ludeña, Verónica; San-Segundo, Rubén; Montero, Juan Manuel; Córdoba, Ricardo de; Ferreiros, Javier; Pardo, José Manuel

doi:10.1016/j.csl.2011.09.003

Cited by 15 publications

(10 citation statements)

References 22 publications

(35 reference statements)

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“…Another interesting view is portrayed in Figure 6, conceived to relate top 10 authors and classifications. It facilitates to determine that Jemni [125][126][127], San Segundo [128,129], and López-Ludeña [130][131][132][133] are mandatory references when dealing with automatic translation. On the other hand, Braffort [95,134] and Kacorri [135,136], who also integrate the top 10 authors, are more "citable" when dealing with corpus conformation or animation techniques, respectively.…”

Section: Topics (Rq2)mentioning

confidence: 99%

A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

et al. 2019

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Sign languages (SL) are the first language for most deaf people. Consequently, bidirectional communication among deaf and non-deaf people has always been a challenging issue. Sign language usage has increased due to inclusion policies and general public agreement, which must then become evident in information technologies, in the many facets that comprise sign language understanding and its computational treatment. In this study, we conduct a thorough systematic mapping of translation-enabling technologies for sign languages. This mapping has considered the most recommended guidelines for systematic reviews, i.e., those pertaining software engineering, since there is a need to account for interdisciplinary areas of accessibility, human computer interaction, natural language processing, and education, all of them part of ACM (Association for Computing Machinery) computing classification system directly related to software engineering. An ongoing development of a software tool called SYMPLE (SYstematic Mapping and Parallel Loading Engine) facilitated the querying and construction of a base set of candidate studies. A great diversity of topics has been studied over the last 25 years or so, but this systematic mapping allows for comfortable visualization of predominant areas, venues, top authors, and different measures of concentration and dispersion. The systematic review clearly shows a large number of classifications and subclassifications interspersed over time. This is an area of study in which there is much interest, with a basically steady level of scientific publications over the last decade, concentrated mainly in the European continent. The publications by country, nevertheless, usually favor their local sign language.

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Section: Topics (Rq2)mentioning

confidence: 99%

A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

et al. 2019

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“…However, a proper classification of errors, which should take into account the linguistic phenomena involved in the error, can be helpful in choosing more appropriate system architecture and in the development of new rules for RBMT systems and resources for translation. As an exception, López-Ludeña et al (2012) contains an analysis of errors for the Spanish-to-LSE SMT system described in Section 3 applied to a highly specific domain. In that work, the main sources of errors are reported in order of decreasing importance as follows: (a) the differences in the number of words and signs for parallel sentences, due to the absence in Spanish of pronominal subjects, and the absence in LSE of definite articles, prepositions, and copula, and the different realisations of plural, and others; (b) the differences in word order (SVO versus SOV); (c) the incorrect generation of classifier predicates; (d) out of vocabulary words; and (e) specific names in LSE periphrastic expressions.…”

Section: Analysis Of Errorsmentioning

confidence: 99%

“…Rules define short-and long-scope relationships between concepts or signs. Categorisation, as described in (López-Ludeña et al, 2011b), consists of tagging a word as non-relevant or giving it a list of manually defined tags such as lemmas or word categories. For the SMT approach, two methods were evaluated: the Moses system and a Stochastic FiniteState Transducer using the GIATI algorithm (Casacuberta and Vidal, 2004).…”

Section: Review Of Machine Translation Systems To Sign Languages and mentioning

confidence: 99%

A rule-based translation from written Spanish to Spanish Sign Language glosses

Porta

López-Colino

Tejedor

et al. 2014

Computer Speech & Language

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This is the author’s version of a work that was accepted for publication in Computer Speech and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech and Language, 28, 3 (2015) DOI: 10.1016/j.csl.2013.10.003One of the aims of Assistive Technologies is to help people with disabilities to communicate with others and to provide means of access to information. As an aid to Deaf people, we present in this work a production-quality rule-based machine system for translating from Spanish to Spanish Sign Language (LSE) glosses, which is a necessary precursor to building a full machine translation system that eventually produces animation output. The system implements a transfer-based architecture from the syntactic functions of dependency analyses. A sketch of LSE is also presented. Several topics regarding translation to sign languages are addressed: the lexical gap, the bootstrapping of a bilingual lexicon, the generation of word order for topic-oriented languages, and the treatment of classifier predicates and classifier names. The system has been evaluated with an open-domain testbed, reporting a 0.30 BLEU (BiLingual Evaluation Understudy) and 42% TER (Translation Error Rate). These results show consistent improvements over a statistical machine translation baseline, and some improvements over the same system preserving the word order in the source sentence. Finally, the linguistic analysis of errors has identified some differences due to a certain degree of structural variation in LSE

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“…For LSE, it is important to highlight the authors' experience in developing speech into LSE translation systems in several domains (San Segundo et al, 2008;San Segundo et al, 2011;López-Ludeña et al, 2011;. This kind of system can complement a Sign Language into Speech translation system, allowing a two-direction interaction (Cemil et al, 2011;Ibarguren et al, 2010).…”

mentioning

confidence: 99%

“…This technology has been used for translating both panel information and spoken Spanish into LSE in real interactions between a deaf person and a hearing person without an interpreter: deaf customers and bus company employees that provide bus information. The method and the system used in this research work, has been developed during several years in previous research projects (San Segundo et al, 2008;San Segundo et al, 2011;López-Ludeña et al, 2011;.…”

mentioning

confidence: 99%