We introduce a new multilingual resource containing judgments about nominal compound compositionality in English, French and Portuguese. It covers 3 × 180 noun-noun and adjective-noun compounds for which we provide numerical compositionality scores for the head word, for the modifier and for the compound as a whole, along with possible paraphrases. This resource was constructed by native speakers via crowdsourcing. It can serve as basis for evaluating tasks such as lexical substitution and compositionality prediction.

This study presents SMILLE, a system that draws on the Noticing Hypothesis and on input enhancements, addressing the lack of salience of grammatical information in online documents chosen by a given user. By means of input enhancements, the system can draw the user's attention to grammar, which could possibly lead to a higher intake per input ratio for metalinguistic information. The system receives as input an online document and submits it to a combined processing of parser and hand-written rules for detecting its grammatical structures. The input text can be freely chosen by the user, providing a more engaging experience and reflecting the user's interests. The system can enhance a total of 107 fine-grained types of grammatical structures that are based on the CEFR. An evaluation of some of those structures resulted in an overall precision of 87%.

Agradeço, preponderantemente, à minha família; Silvia, Guilherme e Isabela, pelo apoio, compreensão e amor dedicados ao longo dos espetaculares últimos sete anos e de muitos outros que ainda virão. Aos meus pais, Vital e Roselin; e irmãos, Mauricio e Gabriel, pela construção da base de apoio na qual se é possível desenvolver uma vida. À todos os demais parentes e familiares, por compartilharem dos bons momentos juntos no

Verbal multiword expressions (VMWEs) such as to make ends meet require special attention in NLP and linguistic research, and annotated corpora are valuable resources for studying them. Corpora annotated with VMWEs in several languages, including Brazilian Portuguese, were made freely available in the PARSEME shared task. The goal of this paper is to describe and analyze this corpus in terms of the characteristics of annotated VMWEs in Brazilian Portuguese. First, we summarize and exemplify the criteria used to annotate VMWEs. Then, we analyze their frequency, average length, discontinuities and variability. We further discuss challenging constructions and borderline cases. We believe that this analysis can improve the annotated corpus and its results can be used to develop systems for automatic VMWE identification.

Although emotions are universal concepts, transferring the different shades of emotion from one language to another may not always be straightforward for human translators, let alone for machine translation systems. Moreover, the cognitive states are established by verbal explanations of experience which is shaped by both the verbal and cultural contexts. There are a number of verbal contexts where expression of emotions constitutes the pivotal component of the message. This is particularly true for User-Generated Content (UGC) which can be in the form of a review of a product or a service, a tweet, or a social media post. Recently, it has become common practice for multilingual websites such as Twitter to provide an automatic translation of UGC to reach out to their linguistically diverse users. In such scenarios, the process of translating the user's emotion is entirely automatic with no human intervention, neither for post-editing nor for accuracy checking. In this research, we assess whether automatic translation tools can be a successful real-life utility in transferring emotion in user-generated multilingual data such as tweets. We show that there are linguistic phenomena specific of Twitter data that pose a challenge in translation of emotions in different languages. We summarise these challenges in a list of linguistic features and show how frequent these features are in different language pairs. We also assess the capacity of commonly used methods for evaluating the performance of an MT system with respect to the preservation of emotion in the source text.