Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Marketing and Advertising Translation: Humans vs Machines in the field of cosmetics

This undergraduate thesis focuses on a very specific field of specialized translation: advertising and marketing translation. Indeed, the high degree of specialization involved in this activity provides a testing ground for a reconsideration of the importance of the human translator and a reformulation of their role. The constant development of new technologies creates ever more sophisticated translation programs, which in turn revives the long-standing machine vs human translation debate. The aim of this project is to conduct a practical exercise targeted at verifying whether specialization in translation always requires the supervision of humans equipped with the relevant linguistic knowledge and technical background, or whether, on the contrary, machine translation can at present provide valid enough results and a sufficient level of reliability.El presente Trabajo de Fin de Grado se centra en un campo muy concreto de la traducción especializada: la traducción para la publicidad y la mercadotecnia. De hecho, el alto grado de especialización que implica esta actividad proporciona un campo de pruebas para una reconsideración de la importancia del traductor humano y una reformulación de su papel. El desarrollo creciente e ininterrumpido de las nuevas tecnologías está produciendo programas de traducción cada vez más sofisticados, lo que a su vez reaviva el viejo debate que confronta la traducción humana y la traducción automática. El objetivo de este proyecto es llevar a cabo un ejercicio práctico destinado a verificar si la especialización en la traducción siempre requiere la supervisión de personas con la formación lingüística y los conocimientos técnicos pertinentes, o si, por el contrario, la traducción automática puede en la actualidad proporcionar por si sola resultados suficientes y un nivel suficiente de fiabilidad.Grado en Estudios Inglese

CLEAR: a credible method to evaluate website archivability

Web archiving is crucial to ensure that cultural, scientific and social heritage on the web remains accessible and usable over time. A key aspect of the web archiving process is optimal data extraction from target websites. This procedure is difficult for such reasons as, website complexity, plethora of underlying technologies and ultimately the open-ended nature of the web. The purpose of this work is to establish the notion of Website Archivability (WA) and to introduce the Credible Live Evaluation of Archive Readiness (CLEAR) method to measure WA for any website. Website Archivability captures the core aspects of a website crucial in diagnosing whether it has the potentiality to be archived with completeness and accuracy. An appreciation of the archivability of a web site should provide archivists with a valuable tool when assessing the possibilities of archiving material and in- uence web design professionals to consider the implications of their design decisions on the likelihood could be archived. A prototype application, archiveready.com, has been established to demonstrate the viabiity of the proposed method for assessing Website Archivability

Producing Monolingual and ParallelWeb Corpora at the Same Time – SpiderLing and Bitextor’s Love Affair

This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For gathering linguistically relevant data from top-level domains we use the SpiderLing crawler, modified to crawl data written in multiple languages. The output of this process is then fed to Bitextor, a tool for harvesting parallel data from a collection of documents. We call the system combining these two tools Spidextor, a blend of the names of its two crucial parts. We evaluate the described approach intrinsically by measuring the accuracy of the extracted bitexts from the Croatian top-level domain .hr and the Slovene top-level domain .si, and extrinsically on the English–Croatian language pair by comparing an SMT system built from the crawled data with third-party systems. We finally present parallel datasets collected with our approach for the English–Croatian, English–Finnish, English–Serbian and English–Slovene language pairs.This research is supported by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (AbuMaTran)

Harnessing sense-level information for semantically augmented knowledge extraction

Nowadays, building accurate computational models for the semantics of language lies at the very core of Natural Language Processing and Artificial Intelligence. A first and foremost step in this respect consists in moving from word-based to sense-based approaches, in which operating explicitly at the level of word senses enables a model to produce more accurate and unambiguous results. At the same time, word senses create a bridge towards structured lexico-semantic resources, where the vast amount of available machine-readable information can help overcome the shortage of annotated data in many languages and domains of knowledge. This latter phenomenon, known as the knowledge acquisition bottlneck, is a crucial problem that hampers the development of large-scale, data-driven approaches for many Natural Language Processing tasks, especially when lexical semantics is directly involved. One of these tasks is Information Extraction, where an effective model has to cope with data sparsity, as well as with lexical ambiguity that can arise at the level of both arguments and relational phrases. Even in more recent Information Extraction approaches where semantics is implicitly modeled, these issues have not yet been addressed in their entirety. On the other hand, however, having access to explicit sense-level information is a very demanding task on its own, which can rarely be performed with high accuracy on a large scale. With this in mind, in ths thesis we will tackle a two-fold objective: our first focus will be on studying fully automatic approaches to obtain high-quality sense-level information from textual corpora; then, we will investigate in depth where and how such sense-level information has the potential to enhance the extraction of knowledge from open text. In the first part of this work, we will explore three different disambiguation scenar- ios (semi-structured text, parallel text, and definitional text) and devise automatic disambiguation strategies that are not only capable of scaling to different corpus sizes and different languages, but that actually take advantage of a multilingual and/or heterogeneous setting to improve and refine their performance. As a result, we will obtain three sense-annotated resources that, when tested experimentally with a baseline system in a series of downstream semantic tasks (i.e. Word Sense Disam- biguation, Entity Linking, Semantic Similarity), show very competitive performances on standard benchmarks against both manual and semi-automatic competitors. In the second part we will instead focus on Information Extraction, with an emphasis on Open Information Extraction (OIE), where issues like sparsity and lexical ambiguity are especially critical, and study how to exploit at best sense-level information within the extraction process. We will start by showing that enforcing a deeper semantic analysis in a definitional setting enables a full-fledged extraction pipeline to compete with state-of-the-art approaches based on much larger (but noisier) data. We will then demonstrate how working at the sense level at the end of an extraction pipeline is also beneficial: indeed, by leveraging sense-based techniques, very heterogeneous OIE-derived data can be aligned semantically, and unified with respect to a common sense inventory. Finally, we will briefly shift the focus to the more constrained setting of hypernym discovery, and study a sense-aware supervised framework for the task that is robust and effective, even when trained on heterogeneous OIE-derived hypernymic knowledge