We're partnering again with ACM NY (www.meetup.com/ACM-NY) for two talks about NLP!
Thank you to NYC Data Science Academy (www.nycdatascience.com) for hosting us.

A Novel Neural Network Architecture for NLP:

Deep Learning in NLP has been dominated in the past years by recurrent & convolutional models. But other models emerge to improve translation quality & performance.
Alex has developed a translator for his team & clients using a new neural network architecture called the Transformer. Unlike traditional translator models, this one solely focuses on attention instead of recurrence & develops powerful NLP models in a fraction of the training time.
Alex will explain how he built the translator, give a live demo, & discuss how the Transformer is able to overcome pitfalls of RNN models.

Alex Wolf, Data Scientist at Dataiku:

Alex is a Data Scientist at Dataiku, working with clients around the world to organize their data infrastructures & deploy data-driven products into production. Prior to that, he worked on software & business development in the tech industry & studied Computer Science & Statistics at Dartmouth College. He's passionate about the latest developments in Deep Learning/Tech & works at enriching Dataiku's NLP features.

Language Comprehension & Language Generation in Eventful Contexts:

Building AI systems that can process user input, understand it, and
generate an engaging & contextually-relevant output in response, has
been one of the longest-running goals in AI. Humans use a variety of
modalities, such as language & visual cues, to communicate. A major
trigger to our meaningful communications are "events" & how they
cause/enable future events. In this talk, I will present my research
about language comprehension & language generation around events,
with a major focus on commonsense reasoning, world knowledge, and
context modeling. I will focus on multiple context modalities such as
narrative, conversational, & visual.

Nasrin Mostafazadeh, Senior AI Research Scientist at Elemental Cognition:

Nasrin works on the next generation of AI systems that not only comprehend language but also explain their reasoning & answer 'why'. She has previously held research positions at BenevolentAI, Microsoft, & Google, working on various language comprehension tasks. Nasrin got her PhD at the University of Rochester at the conversational interaction & dialogue research group, during which she worked on language understanding in the context of stories, mainly through the lens of events & their causal & temporal relations. She has developed models for tackling various research tasks that push AI toward deeper language understanding with applications ranging from story generation to vision & language.