The study reported in this paper aims to contribute to the development of an annotation scheme for evaluative language, based on Appraisal Theory, that addresses key sources of classification problems. In particular. it aims to develops a unified annotation scheme that proposes (1) a three-component annotation model comprising Appraiser, Appraised and Appraisal Element, (2) the operationalised distinction between Affect and Appreciation governed by a criterion of experiencer salience and a criterion distinguishing personal emotions from evaluations of conduct, and (3) a decision framework for the Judgement-Appreciation distinction structured on the target and lexis types operating through override conditions and substitution tests. The revised framework is illustrated with examples selected from a corpus of news discourse in English and is designed to be replicable across future Appraisal-based studies of evaluative language.

English dominates scientific publishing, which disadvantages researchers who are not native English speakers, especially those in the earlier stages of their careers. Being able to write and engage with scientific content written in their own language would clearly facilitate scientific production. The MaTOS project (Machine Translation for Open Science) seeks to reduce these barriers by developing machine translation tools for scientific documents in English and French. This article presents the design of the MaTOS pipeline for the HAL platform to automatically translate article abstracts, with author validation, to increase the number of bilingual abstracts on the platform. We also report preliminary experiments comparing translation of sentence, three-sentence chunks, and whole abstracts, evaluated using quality estimation metrics.