Diseño estructural y estandarización de planos para la fabricación de furgones, en la empresa Fibercol S.A

El PRFV (plástico reforzado con fibra de vidrio) es un material compuesto, perteneciente al grupo de los plásticos termofijos el cual posee propiedades mecánicas excepcionales para la construcción de diversos productos. Una de las tecnologías que se emplea en la empresa FIBERCOL S.A, es la fabricación de paneles, para la construcción de furgones, los cuales están dispuestos en estructuras sándwich. Estas estructuras proveen al producto, una gran rigidez, manteniendo la relación primordial en el material; resistencia/peso. Las materias primas que se involucran son las siguientes: resina poliéster insaturada reforzada con fibra de vidrio, gel coat, insertos metálicos, y poliuretano, un material de una densidad bastante baja, que además de servir como núcleo estructural, cumple con una característica esencial para la fabricación del producto, y es la de ser un buen aislante térmico, esto, para el transporte de carga que debe de ser conservada, a bajas temperaturas. En la fabricación del producto, es necesario contar con planos confiables para su ensamble posterior, por esto la importancia de hacer un énfasis en las características de los materiales aquí involucrados. Esto, con el objetivo de predecir los espesores y así definir las tolerancias requeridas, para la construcción de este componente. En muchas aplicaciones, aunque el material posee buenas propiedades mecánicas, es necesario utilizar insertos metálicos, con la finalidad de transmitir las cargas, a las cuales se ve sometido el producto y a la vez, para que ayuden a darle a éste, una rigidez y configuración geométrica apropiadaPasantía (Ingeniero Mecánico)-- Universidad Autónoma de Occidente. Facultad de Ingeniería, 2006PregradoIngeniero(a) Mecánico(a

Fostering collective action through participation in natural resource and environmental management: An integrative and interpretative narrative review using the IAD, NAS and SES frameworks

Solving humanity's social-environmental challenges calls for collective action by relevant actors. Hence, involving these actors in the policy process has been deemed both necessary and promising. But how and to what extent can participatory policy interventions (PIs) foster collective action for sustainable environmental and natural resource management? Lab and lab-in-the-field experiments on co-operation in the context of collective action challenges (i.e. social dilemmas) and case study research on participatory processes both offer insights into this question but have hitherto mainly remained unconnected. This article reviews insights from these two streams of literature in tandem, synthesising and analysing them using the institutional analysis and development (IAD) framework in combination with the network of action situations (NAS) framework and the social-ecological systems (SES) framework. We thus perform an integrative and interpretative narrative review to draw a richer and more nuanced picture of PIs: their potential impacts, their (institutional and behavioural) mechanisms and challenges, and caveats and recommendations for their design and implementation. Our review shows that PIs can indeed foster collective action by (a) helping the relevant actors craft suitable and legitimate institutional arrangements and (b) addressing and/or influencing actors' attributes of relevance to collective action, namely their individual and shared understandings, beliefs and preferences. To fulfil this potential, the organisers and sponsors of PIs must address and link to the broader context through soundly designed and implemented processes. Complementary follow-up, enforcement and conflict resolution mechanisms are necessary to nurture, reassure and sustain understandings, beliefs and preferences that undergird trust-building and collective action. The conceptual framework developed for the review can help researchers and practitioners further assess these insights, disentangle PIs' mechanisms and impacts, and integrate the research and practice of participatory governance and collective action

Target-language-driven agglomerative part-of-speech tag clustering for machine translation

This paper presents a method for reducing the set of different tags to be considered by a part-of-speech tagger. The method is based on a clustering algorithm performed over the states of a hidden Markov model, which is initially trained by considering information not only from the source language, but also from the target language, using a new unsupervised technique which has been recently proposed to obtain taggers involved in machine translation systems. Then, a bottom-up agglomerative clustering algorithm groups the states of the hidden Markov model according to a similarity measure based on their transition probabilities; this reduces the complexity by grouping the initial finer tags into coarser ones. The experiments show that part-of-speech taggers using the coarser tags have smaller error rates than those using the initial finest tags; moreover, considering unsupervised information from the target language results in better clusters compared to those unsupervisedly built from source language information only.Work funded by the Spanish Ministry of Science and Technology through project TIC2003-08681-C02-01, and by the Spanish Ministry of Education and Science and the European Social Found through grant BES-2004-4711

Manual d'informàtica i de tecnologies per a la traducció

Este libro cubre la mayor parte de los contenidos de la asignatura Tecnologías de la Traducción que cursara el alumnado de segundo curso del grado en Traducción e Interpretación de la Universitat d'Alacant; también puede ser útil para asignaturas similares en otras universidades (por eso se ha incluido material más avanzado que no se estudia en Tecnologías de la Traducción).Aquest llibre cobreixen la major part dels continguts de l'assignatura Tecnologies de la Traducció que cursara l'alumnat de segon curs del grau en Traducció i Interpretació de la Universitat d’Alacant; també pot ser útil per a assignatures similars en altres universitats (per aixo s’hi ha inclòs material mes avançat que no s'estudia en Tecnologies de la Traducció)

Using target-language information to train part-of-speech taggers for machine translation

Although corpus-based approaches to machine translation (MT) are growing in interest, they are not applicable when the translation involves less-resourced language pairs for which there are no parallel corpora available; in those cases, the rule-based approach is the only applicable solution. Most rule-based MT systems make use of part-of-speech (PoS) taggers to solve the PoS ambiguities in the source-language texts to translate; those MT systems require accurate PoS taggers to produce reliable translations in the target language (TL). The standard statistical approach to PoS ambiguity resolution (or tagging) uses hidden Markov models (HMM) trained in a supervised way from hand-tagged corpora, an expensive resource not always available, or in an unsupervised way through the Baum-Welch expectation-maximization algorithm; both methods use information only from the language being tagged. However, when tagging is considered as an intermediate task for the translation procedure, that is, when the PoS tagger is to be embedded as a module within an MT system, information from the TL can be (unsupervisedly) used in the training phase to increase the translation quality of the whole MT system. This paper presents a method to train HMM-based PoS taggers to be used in MT; the new method uses not only information from the source language (SL), as general-purpose methods do, but also information from the TL and from the remaining modules of the MT system in which the PoS tagger is to be embedded. We find that the translation quality of the MT system embedding a PoS tagger trained in an unsupervised manner through this new method is clearly better than that of the same MT system embedding a PoS tagger trained through the Baum-Welch algorithm, and comparable to that obtained by embedding a PoS tagger trained in a supervised way from hand-tagged corpora.Work funded by the Spanish Ministry of Science and Technology through project TIC2003-08601-C02-01 and by the Spanish Ministry of Education and Science and the European Social Fund through research grant BES-2004-4711 and project TIN2006-15071-C03-01

Participatory interventions for collective action and sustainable resource management: linking actors, situations and contexts through the IAD, NAS and SES frameworks

Overcoming complex environmental challenges demands different forms of stakeholder participation and collective action. While informative and relevant for participatory interventions, the literatures on collective action and participatory governance have largely remained disconnected. We illustrate how the institutional analysis and development (IAD), network of (adjacent) action situation (NAS) and social–ecological system (SES) frameworks can be combined to provide a coherent approach that integrates these literatures, applies their insights and bridges this disconnect. We compare two similar participatory interventions, one in Colombia and one in Peru, whose design and implementation we supported. Transdisciplinary in nature, both sought to foster collective action for watershed management. The frameworks allow us to demarcate, characterise and reflect upon the action situations (ASs) for the collective choice, coordination and knowledge generation that constituted each participatory intervention (i.e. the constituent NAS) and other relevant operational and institutional ASs that lay outside the boundaries of the participatory interventions. These other ASs may not be linked to one another or to the intervention’s constituent NAS, but they influence the outcomes of interest nevertheless, thereby shaping the potential of the participatory interventions for collective action and sustainable natural resource management. The framework then suggests, and our comparative analysis illustrates, that organisers and researchers of participatory interventions, such as multi-actor deliberative platforms and transdisciplinary research projects, should carefully consider, reflect upon and address the constellation of relevant actors, ASs and contexts co-determining the outcomes of interest. Our study demonstrates how the IAD, SES and NAS frameworks can support that endeavour

Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation

We describe a hybridisation strategy whose objective is to integrate linguistic resources from shallow-transfer rule-based machine translation (RBMT) into phrase-based statistical machine translation (PBSMT). It basically consists of enriching the phrase table of a PBSMT system with bilingual phrase pairs matching transfer rules and dictionary entries from a shallow-transfer RBMT system. This new strategy takes advantage of how the linguistic resources are used by the RBMT system to segment the source-language sentences to be translated, and overcomes the limitations of existing hybrid approaches that treat the RBMT systems as a black box. Experimental results confirm that our approach delivers translations of higher quality than existing ones, and that it is specially useful when the parallel corpus available for training the SMT system is small or when translating out-of-domain texts that are well covered by the RBMT dictionaries. A combination of this approach with a recently proposed unsupervised shallow-transfer rule inference algorithm results in a significantly greater translation quality than that of a baseline PBSMT; in this case, the only hand-crafted resource used are the dictionaries commonly used in RBMT. Moreover, the translation quality achieved by the hybrid system built with automatically inferred rules is similar to that obtained by those built with hand-crafted rules.Research funded by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF 2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran)

A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora

Statistical and rule-based methods are complementary approaches to machine translation (MT) that have different strengths and weaknesses. This complementarity has, over the last few years, resulted in the consolidation of a growing interest in hybrid systems that combine both data-driven and linguistic approaches. In this paper, we address the situation in which the amount of bilingual resources that is available for a particular language pair is not sufficiently large to train a competitive statistical MT system, but the cost and slow development cycles of rule-based MT systems cannot be afforded either. In this context, we formalise a new method that uses scarce parallel corpora to automatically infer a set of shallow-transfer rules to be integrated into a rule-based MT system, thus avoiding the need for human experts to handcraft these rules. Our work is based on the alignment template approach to phrase-based statistical MT, but the definition of the alignment template is extended to encompass different generalisation levels. It is also greatly inspired by the work of Sánchez-Martínez and Forcada (2009) in which alignment templates were also considered for shallow-transfer rule inference. However, our approach overcomes many relevant limitations of that work, principally those related to the inability to find the correct generalisation level for the alignment templates, and to select the subset of alignment templates that ensures an adequate segmentation of the input sentences by the rules eventually obtained. Unlike previous approaches in literature, our formalism does not require linguistic knowledge about the languages involved in the translation. Moreover, it is the first time that conflicts between rules are resolved by choosing the most appropriate ones according to a global minimisation function rather than proceeding in a pairwise greedy fashion. Experiments conducted using five different language pairs with the free/open-source rule-based MT platform Apertium show that translation quality significantly improves when compared to the method proposed by Sánchez-Martínez and Forcada (2009), and is close to that obtained using handcrafted rules. For some language pairs, our approach is even able to outperform them. Moreover, the resulting number of rules is considerably smaller, which eases human revision and maintenance.Research funded by Universitat d’Alacant through project GRE11-20, by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF/2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran)

Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation

This paper studies the effects of word-level linguistic annotations in under-resourced neural machine translation, for which there is incomplete evidence in the literature. The study covers eight language pairs, different training corpus sizes, two architectures and three types of annotation: dummy tags (with no linguistic information at all), part-of-speech tags, and morpho-syntactic description tags, which consist of part of speech and morphological features. These linguistic annotations are interleaved in the input or output streams as a single tag placed before each word. In order to measure the performance under each scenario, we use automatic evaluation metrics and perform automatic error classification. Our experiments show that, in general, source-language annotations are helpful and morpho-syntactic descriptions outperform part of speech for some language pairs. On the contrary, when words are annotated in the target language, part-of-speech tags systematically outperform morpho-syntactic description tags in terms of automatic evaluation metrics, even though the use of morpho-syntactic description tags improves the grammaticality of the output. We provide a detailed analysis of the reasons behind this result.Work funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement number 825299, project Global Under-Resourced Media Translation (GoURMET)