Advancements within the field of text simplification (TS) have primarily been within syntactic or lexical simplification. However, conceptual simplification has previously been identified as another field of TS that has the potential to significantly
improve reading comprehension. A first step to measuring conceptual simplification is the classification of concepts as either complex or simple. This research-in-progress paper proposes a new definition of conceptual complexity alongside a simple machine-learning approach that performs a binary classification task to distinguish between simple and complex concepts. It is proposed that this be a first step when developing new text simplification models that operate on a conceptual level.
The development of Translation Technologies, like Translation Memory and Machine Translation, has completely changed the translation industry and translator's workflow in the last decades. Nevertheless, TM and MT have been developed separately until
very recently. This ongoing project will study the external integration of TM and MT, examining if the productivity and post-editing efforts of translators are higher or lower than using only TM. To this end, we will conduct an experiment where Translation students and professional translators will be asked to translate two short texts; then we will check the post-editing efforts (temporal, technical and cognitive efforts) and the quality of the translated texts.
