Perhaps the most important dimension of variation is the language. NLP algorithms are most useful when they apply across many languages. The world has 7097 languages at the time of this writing, according to the online Ethnologue catalog (Simons and Fennig, 2018) . It is important to test algorithms on more than one language, and particularly on languages with different properties; by contrast there is an unfortunate current tendency for NLP algorithms to be developed or tested just on English (Bender, 2019) . Even when algorithms are developed beyond English, they tend to be developed for the official languages of large industrialized nations (Chinese, Spanish, Japanese, German etc.), but we don't want to limit tools to just these few languages. Furthermore, most languages also have multiple varieties, often spoken in different regions or by different social groups. Thus, for example, if we're processing text that uses features of African American English (AAE) or AAE African American Vernacular English (AAVE) -the variations of English used by millions of people in African American communities (King 2020) -we must use NLP tools that function with features of those varieties. Twitter posts might use features often used by speakers of African American English, such as constructions like iont (I don't in Mainstream American English (MAE)), or talmbout corresponding MAE to MAE talking about, both examples that influence word segmentation (Blodgett et al. 2016 , Jones 2015 .