- Lane Schwartz, Francis M. Tyers, Lori S. Levin, Christo Kirov, Patrick Littell, Chi-kiu (羅致翹) Lo, Emily Prudhommeaux, Hyunji Hayley Park, K. Steimel, Rebecca Knowles, J. Micher, Lonny Strunk, Han Liu, Coleman Haley, Katherine J. Zhang, Robbie Jimmerson, Vasilisa Andriyanets, Aldrian Obaja Muis, Naoki Otani, J. Park, Zhisong Zhang. 2020. Neural Polysynthetic Language Modelling. Abstract: Research in natural language processing commonly assumes that approaches that work well for English and and other widely-used languages are "language agnostic". In high-resource languages, especially those that are analytic, a common approach is to treat morphologically-distinct variants of a common root as completely independent word types. This assumes, that there are limited morphological inflections per root, and that the majority will appear in a large enough corpus, so that the model can adequately learn statistics about each form. Approaches like stemming, lemmatization, or subword segmentation are often used when either of those assumptions do not hold, particularly in the case of synthetic languages like Spanish or Russian that have more inflection than English. 
