Agata Savary – Diversity quantification in natural language processing скачать в хорошем качестве

Agata Savary – Diversity quantification in natural language processing

The concept of diversity has received increased consideration in Natural Language Processing (NLP) in recent years. This is due to various motivations like promoting equity and inclusion, approximating human linguistic behavior, and increasing systems’ performance. Diversity has however often been addressed in an ad hoc manner in NLP, and with few explicit links to other domains where this notion is better theorized. We survey articles in the ACL Anthology from the past 6 years, with "diversity" or "diverse" in their title. We find a wide range of settings in which diversity is quantified, often highly specialized and using inconsistent terminology. We put forward a unified taxonomy of why, what on, where, and how diversity is measured in NLP. Diversity measures are cast upon a unified framework from ecology and economy (Stirling, 2007) with 3 dimensions of diversity: variety, balance and disparity. We discuss the trends which emerge due to this systematized approach. We believe that this study paves the way towards a better formalization of diversity in NLP, which should bring a better understanding of this notion and a better comparability between various approaches. This is a joint work with Louis Estève (Université Paris-Saclay, France), Marie-Catherine de Marneffe (Université Catholique de Louvain, Belgium), Nurit Melnik (The Open University of Israel) and Olha Kanishcheva (Jena University in Germany and the STEP University in Ukraine), within the framework of the UniDive COST Action on Universality, Diversity and Idiosyncrasy in Language Technology.