Perceived difficulty and time effectiveness in translation: a comparison of machine translation post-editing and translation memory use

Con il presente studio si è inteso analizzare l’impatto dell’utilizzo di una memoria di traduzione (TM) e del post-editing (PE) di un output grezzo sul livello di difficoltà percepita e sul tempo necessario per ottenere un testo finale di alta qualità. L’esperimento ha coinvolto sei studenti, di madrelingua italiana, del corso di Laurea Magistrale in Traduzione Specializzata dell’Università di Bologna (Vicepresidenza di Forlì). I partecipanti sono stati divisi in tre coppie, a ognuna delle quali è stato assegnato un estratto di comunicato stampa in inglese. Per ogni coppia, ad un partecipante è stato chiesto di tradurre il testo in italiano usando la TM all’interno di SDL Trados Studio 2011. All’altro partecipante è stato chiesto di fare il PE completo in italiano dell’output grezzo ottenuto da Google Translate. Nei casi in cui la TM o l’output non contenevano traduzioni (corrette), i partecipanti avrebbero potuto consultare Internet. Ricorrendo ai Think-aloud Protocols (TAPs), è stato chiesto loro di riflettere a voce alta durante lo svolgimento dei compiti. È stato quindi possibile individuare i problemi traduttivi incontrati e i casi in cui la TM e l’output grezzo hanno fornito soluzioni corrette; inoltre, è stato possibile osservare le strategie traduttive impiegate, per poi chiedere ai partecipanti di indicarne la difficoltà attraverso interviste a posteriori. È stato anche misurato il tempo impiegato da ogni partecipante. I dati sulla difficoltà percepita e quelli sul tempo impiegato sono stati messi in relazione con il numero di soluzioni corrette rispettivamente fornito da TM e output grezzo. È stato osservato che usare la TM ha comportato un maggior risparmio di tempo e che, al contrario del PE, ha portato a una riduzione della difficoltà percepita. Il presente studio si propone di aiutare i futuri traduttori professionisti a scegliere strumenti tecnologici che gli permettano di risparmiare tempo e risorse.

Bitext alignment: building and evaluating a bilingual corpus and translation memory of academic course descriptions

Following the internationalization of contemporary higher education, academic institutions based in non-English speaking countries are increasingly urged to produce contents in English to address international prospective students and personnel, as well as to increase their attractiveness. The demand for English translations in the institutional academic domain is consequently increasing at a rate exceeding the capacity of the translation profession. Resources for assisting non-native authors and translators in the production of appropriate texts in L2 are therefore required in order to help academic institutions and professionals streamline their translation workload. Some of these resources include: (i) parallel corpora to train machine translation systems and multilingual authoring tools; and (ii) translation memories for computer-aided tools. The purpose of this study is to create and evaluate reference resources like the ones mentioned in (i) and (ii) through the automatic sentence alignment of a large set of Italian and English as a Lingua Franca (ELF) institutional academic texts given as equivalent but not necessarily parallel (i.e. translated). In this framework, a set of aligning algorithms and alignment tools is examined in order to identify the most profitable one(s) in terms of accuracy and time- and cost-effectiveness. In order to determine the text pairs to align, a sample is selected according to document length similarity (characters) and subsequently evaluated in terms of extent of noisiness/parallelism, alignment accuracy and content leverageability. The results of these analyses serve as the basis for the creation of an aligned bilingual corpus of academic course descriptions, which is eventually used to create a translation memory in TMX format.

Quality and Aspect based Argument Generation

Natural Language Processing has always been one of the most popular topics in Artificial Intelligence. Argument-related research in NLP, such as argument detection, argument mining and argument generation, has been popular, especially in recent years. In our daily lives, we use arguments to express ourselves. The quality of arguments heavily impacts the effectiveness of our communications with others. In professional fields, such as legislation and academic areas, arguments of good quality play an even more critical role. Therefore, argument generation with good quality is a challenging research task that is also of great importance in NLP. The aim of this work is to investigate the automatic generation of arguments with good quality, according to the given topic, stance and aspect (control codes). To achieve this goal, a module based on BERT [17] which could judge an argument's quality is constructed. This module is used to assess the quality of the generated arguments. Another module based on GPT-2 [19] is implemented to generate arguments. Stances and aspects are also used as guidance when generating arguments. After combining all these models and techniques, the ranks of the generated arguments could be acquired to evaluate the final performance. This dissertation describes the architecture and experimental setup, analyzes the results of our experimentation, and discusses future directions.

Computer aided design and finite element modeling to predict bulk mechanical properties of bone scaffolds using triply periodic minimal surfaces (progettazione assistita al calcolatore ed analisi con il metodo degli elementi finiti per predire il comportamento meccanico di scaffolds ossei creati con l'uso di superfici minime periodiche in tre direzioni)

The aim of Tissue Engineering is to develop biological substitutes that will restore lost morphological and functional features of diseased or damaged portions of organs. Recently computer-aided technology has received considerable attention in the area of tissue engineering and the advance of additive manufacture (AM) techniques has significantly improved control over the pore network architecture of tissue engineering scaffolds. To regenerate tissues more efficiently, an ideal scaffold should have appropriate porosity and pore structure. More sophisticated porous configurations with higher architectures of the pore network and scaffolding structures that mimic the intricate architecture and complexity of native organs and tissues are then required. This study adopts a macro-structural shape design approach to the production of open porous materials (Titanium foams), which utilizes spatial periodicity as a simple way to generate the models. From among various pore architectures which have been studied, this work simulated pore structure by triply-periodic minimal surfaces (TPMS) for the construction of tissue engineering scaffolds. TPMS are shown to be a versatile source of biomorphic scaffold design. A set of tissue scaffolds using the TPMS-based unit cell libraries was designed. TPMS-based Titanium foams were meant to be printed three dimensional with the relative predicted geometry, microstructure and consequently mechanical properties. Trough a finite element analysis (FEA) the mechanical properties of the designed scaffolds were determined in compression and analyzed in terms of their porosity and assemblies of unit cells. The purpose of this work was to investigate the mechanical performance of TPMS models trying to understand the best compromise between mechanical and geometrical requirements of the scaffolds. The intention was to predict the structural modulus in open porous materials via structural design of interconnected three-dimensional lattices, hence optimising geometrical properties. With the aid of FEA results, it is expected that the effective mechanical properties for the TPMS-based scaffold units can be used to design optimized scaffolds for tissue engineering applications. Regardless of the influence of fabrication method, it is desirable to calculate scaffold properties so that the effect of these properties on tissue regeneration may be better understood.