The State and Fate of Linguistic Diversity and Inclusion in the NLP World

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6282–6293 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 6282 The State and Fate of Linguistic Diversity and Inclusion in the NLP World Pratik Joshi∗ Sebastin Santy∗ Amar Budhiraja∗ Kalika Bali Monojit Choudhury Microsoft Research, India {t-prjos, t-sesan, amar.budhiraja, kalikab, monojitc}@microsoft.com Abstract Language technologies contribute to promot- ing multilingualism and linguistic diversity around the world. However, only a very small number of the over 7000 languages of the world are represented in the rapidly evolving language technologies and applications. In this paper we look at the relation between the types of languages, resources, and their rep- resentation in NLP conferences to understand the trajectory that different languages have followed over time. Our quantitative inves- tigation underlines the disparity between lan- guages, especially in terms of their resources, and calls into question the “language agnostic” status of current models and systems. Through this paper, we attempt to convince the ACL community to prioritise the resolution of the predicaments highlighted here, so that no lan- guage is left behind. 1 The Questions Languages X and Y are the official languages of two different countries; they have around 29M and 18M native speakers, and 2M and 5.5K Wikipedia articles, respectively. X is syntactically quite sim- ilar to English, though uses dimunitives and has grammatical gender. Y, on the other hand, has a different word order from English, and has a rare typological feature - generally it is a head-final lan- guage, but noun phrases are head-initial. It also features full and partial reduplication. 69 items on LDC and ELRA contain data in X, whereas for Y there are only 2 items. X boasts of some of the best online machine translation systems, whereas Y is supported by very few online MT systems and that too with far inferior translation quality. Fig- ure 1 shows the number of papers in conferences (ACL, NAACL, EACL, EMNLP, LREC, WS) that ∗Authors contributed equally to the work. https://microsoft.github.io/linguisticdiversity (a) ACL + NAACL + EACL + EMNLP (b) LREC + WS Figure 1: Number of papers with mentions of X and Y language for two sets of conferences. mention X and Y in the paper, across the years. As you can see, while X has a steady and growing trend of research, our community has been mostly oblivious to Y, until recently when some of the zero-shot learning papers have started mentioning it. Can you guess what X and Y are? Regardless of whether you can guess the exact answer, most NLP researchers surely know of (and might even speak) several languages which are in the same boat as X; languages which have a large amount of resources and therefore access to the benefits of the current NLP breakthroughs, and languages like Y; those which lack resources and consequently the attention of the NLP community, despite having similar speaker base sizes and typo- logically diverse features. You probably have come across the issue …

Content truncated to 500 words