Natural language processing: Understanding the current landscape

Medha Patki* and Prajna Soni*

Edited by Soumya Kannan

Interview | Aug. 29 2022


DOI: 10.38105/spr.pvh55o2k8e


We spoke with two researchers in Natural Language Processing (NLP) to understand their perspective on the current landscape of NLP – the challenges, successes, and implications of the technology today.

Kathleen Siminyu is an AI Researcher focused on NLP for African languages. She is currently a Machine Learning Fellow at the Mozilla Foundation, supporting the development of a Kiswahili Common Voice dataset and building speech transcription models for end-use cases in the agricultural and financial domains. In her NLP research, Kathleen has previously worked on speech transcription for Luhya languages and contributed to machine translation for Kenyan languages as part of Masakhane. Before joining Mozilla, Kathleen was Regional Coordinator of AI4D Africa, where she worked with ML and artificial intelligence (AI) communities in Africa to run various programs.

Chris Tanner is the Head of R&D at Kensho, and he holds a joint appointment at MIT, where he teaches NLP and machine learning (ML). At Kensho, he oversees NLP research, including document layout analysis, summarization, speech recognition, entity linking, and language modeling. Previously, Chris taught and advised graduate students at Harvard, and prior to that, he served as Associate Staff Researcher at MIT Lincoln Laboratory. He obtained his Ph.D. from Brown University.

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Medha Patki

Harvard Kennedy School, Harvard University, Cambridge, MA

Prajna Soni

Institute of Data, Systems & Society, MIT, Cambridge, MA