Alignment of Multilingual Embeddings to Estimate Job Similarities in Online Labour Market

In recent years, word embeddings (WEs) have proven relevant for studying differences and similarities among job professions and skills required by the labour market across countries, providing valuable insights about the labour market dynamics to support policy and decision-making. In such a scenario, aligning WEs constructed across different countries and languages becomes key to allowing experts to reason on the labour market, catching technological and cultural shifts across borders. This paper proposes MEAL, an unsupervised method for aligning monolingual embeddings. Our approach selects a seed lexicon of anchors, i.e. words with the same meaning in both corpora that will be used as pivots in the alignment, without assuming a priori semantic similarities. Indeed, unlike previous literary works, to asses this relationship MEAL takes into account the semantic similarity between the neighbour of the two words in the WE space. Particularly, it chooses optimal anchors that are less susceptible to meaning shift. We deploy MEAL within the research framework of a European H-2020 Project that aims to use AI technologies to predict the future of the European labour market. Specifically, we apply it to the embeddings we train on 7+ millions of Online Job Advertisements (OJAs) collected in 2022. As a main outcome, MEAL allows stakeholders and policymakers (i) to estimate job similarities in Online Labour Markets across Europe, facilitating the assessment of how well these markets align with the taxonomy outlined by the official European Skills and Competences taxonomy, and (ii) to obtain indicators to support a data-driven policy design at a very fine-grained territorial level.