We approach the task of assessing the suitability of a source text for translation by transferring the knowledge from established MT evaluation metrics to a model able to predict MT quality a priori from the source text alone. To open the door to experiments in this regard, we depart from reference English-German parallel corpora to build a corpus of 14,253 source text-quality score tuples. The tuples include four state-of-the-art metrics: cushLEPOR, BERTScore, COMET, and TransQuest. With this new resource at hand, we fine-tune XLM-RoBERTa, both in a single-task and a multi-task setting, to predict these evaluation scores from the source text alone. Results for this methodology are promising, with the single-task model able to approximate well-established MT evaluation and quality estimation metrics - without looking at the actual machine translations - achieving low RMSE values in the [0.1-0.2] range and Pearson correlation scores up to 0.688.

Spotting hate speech in social media posts is crucial to increase the civility of the Web and has been thoroughly explored in the NLP community. For the first time, we introduce a multilingual corpus for the analysis and identification of hate speech in the domain of inceldom, built from incel Web forums in English and Italian, including expert annotation at the post level for two kinds of hate speech: misogyny and racism. This resource paves the way for the development of mono- and cross-lingual models for (a) the identification of hateful (misogynous and racist) posts and (b) the forecasting of the amount of hateful responses that a post is likely to trigger. Our experiments aim at improving the performance of Transformer-based models using masked language modeling pre-training and dataset merging. The results show that these strategies boost the models’ performance in all settings (binary classification, multi-label classification and forecasting), especially in the cross-lingual scenarios.

This paper describes an approach to translating course unit descriptions from Italian and German into English, using a phrase-based machine translation (MT) system. The genre is very prominent among those requiring translation by universities in European countries in which English is a non-native language. For each language combination, an in-domain bilingual corpus including course unit and degree program descriptions is used to train an MT engine, whose output is then compared to a baseline engine trained on the Europarl corpus. In a subsequent experiment, a bilingual terminology database is added to the training sets in both engines and its impact on the output quality is evaluated based on BLEU and post-editing score. Results suggest that the use of domain-specific corpora boosts the engines quality for both language combinations, especially for German-English, whereas adding terminological resources does not seem to bring notable benefits.