Natural language processing (NLP) researchers develop models of grammar, meaning and communication based on written text. Due to task and data differences, what is considered text can vary substantially across studies. A conceptual framework for systematically capturing these differences is lacking. We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. Towards that goal, we propose common terminology to discuss the production and transformation of textual data, and introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling. We apply this taxonomy to survey existing work that extends the notion of text beyond the conservative language-centered view. We outline key desiderata and challenges of the emerging inclusive approach to text in NLP, and suggest community-level reporting as a crucial next step to consolidate the discussion.

Last week I wrote about an AI startup that's building technology that can alter, in real time, the accent of someone's speech. But what if the AI goal instead is to make it possible for people speaking in whatever way they do, to be understood just as they are, and to remove some of the bias inherent in a lot of AI systems in the process? There's a major need for that, too, and now a UK startup called Speechmatics -- which has built AI to translate speech to text, regardless of the accent or how the person speaks -- is announcing $62 million in funding to expand its business. Susquehanna Growth Equity out of the U.S. led the round with UK investors AlbionVC and IQ Capital also participating. This is Series B is a big step up for Speechmatics.

Issues around ethical AI have garnered more attention over the past several years. Tech giants from Facebook to Google to Microsoft have already established and published principles to demonstrate to stakeholders -- customers, employees, and investors -- that they understand the importance of ethical or responsible AI. So it was a bit of a black eye last year when the co-head of Google's Ethical AI group, Timnit Gebru, was fired following a dispute with management over a scholarly paper she coauthored and was scheduled to deliver at a conference. Now Gebru has established her own startup focused on ethical AI. The Distributed Artificial Intelligence Research Institute (DAIR) produces interdisciplinary AI research, according to the organization.