In the middle of November, RGCL welcomed Johanna Monti, an Associate Professor of Modern Languages Teaching at the “L’Orientale”University of Naples. Her research activities are in the field of hybrid approaches to Machine Translation and NLP applications.  Whilst Johann was here, she gave two lectures on Multi-word Expressions and Gender Issues in Machine Translation.  The lectures were well received and also attended by the Research Group’s MA students.

TITLE: Parseme-It Corpus: An annotated Corpus of Verbal Multiword Expressions in Italian

ABSTRACT:  This talk outlines the development of a new language resource for Italian, namely the PARSEME-It VMWE corpus, annotated with Italian MWEs of a particular class: verbal multiword expressions (VMWE). The PARSEME-It VMWE corpus has been developed by the PARSEME-IT research group in the framework of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (Savary et al., 2017), a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for verbal multiword expressions in 18 languages, among which also the Italian language is represented. Notably, multiword expressions represent a difficult lexical construction to identify, model and treat by Natural Language Processing (NLP) tools, such as parsers, machine translation engines among others, mainly due to their non-compositional property. In particular, among multiword expressions verbal ones are particularly challenging because they have different syntactic structures (prendere una decisione ’make a decision’, decisioni prese precedentemente ’decisions made previously’), may be continuous and discontinuous (andare e venire versus andare in malora in Luigi ha fatto andare la societ`a in malora), may have a literal and figurative meaning (abboccare all’amo ’bite the hook’ or ’be deceived’). The talk will  describe the state of the art in VMWE annotation and identification for the Italian language, the methodology, the Italian VMWE categories taken into account for the annotation task, the corpus and the annotation process  and the results.

TITLE: Gender Issues in Machine Translation

ABSTRACT:  Machine Translation is one of most widely used Artificial Intelligence applications on the Internet: it is so widespread in online services of various types that sometimes users do not realize that they are using the results of an automatic translation process- In spite of the remarkable progress achieved in this field over the last twenty years thanks to the enhanced calculating capacity of computers and advanced technologies in the field of Natural Language Processing (NLP), machine translation systems, even the most widely used ones on the net such as for example Google Translate, have high error rates.. Among the most frequent problems in the state-of-the-art MT systems, either based on linguistic data like Systran, statistical approaches like Google Translation or the recent neural approach, translation of gender still represents a recurrent source of mistranslations: incorrect gender attribution to proforms (personal pronouns, relative pronouns, among others), reproduction of gender stereotypes and overuse of male pronouns are among the most frequent problems in MT.