What we're about

This group is for anyone working in (or interested in) Natural Language Processing. Our Washington, D.C. area programs will be an opportunity for folks to network, give presentations about their work or research projects, learn about the latest advancements in our field, and exchange ideas or brainstorm, but we'll also meet online so this meetup is for everyone! Topics may include computational linguistics, machine learning, text analytics, data mining, information extraction, speech processing, sentiment analysis, and much more.

If you're actively involved in NLP or just want to learn more about it, please join us!

Don't forget to follow us on Twitter: @DCNLP (https://twitter.com/DCNLP)

Upcoming events (1)

Learning the Difference that Makes a Difference: Counterfactually Augmented Data

Our June 2, 2021 program features Zack Lipton from Carnegie Mellon University, speaking on "Learning the Difference that Makes a Difference with Counterfactually Augmented Data." The program starts at 1 pm US-Eastern / 10 am US-Pacific / 6 pm UK / 7 pm CEST.

Abstract: Despite alarm over the reliance of machine learning systems on so-called spurious patterns, the term lacks coherent meaning in standard statistical frameworks. However, the language of causality offers clarity: spurious associations are due to confounding (e.g., a common cause), but not direct or indirect causal effects. Inspired by this literature (and borrowing from it gesturally), we address natural language processing, introducing methods and resources for training models less sensitive to spurious patterns. Given documents and their initial labels, we task humans with revising each document so that it (i) accords with a counterfactual target label; (ii) retains internal coherence; and (iii) avoids unnecessary changes. Interestingly, on sentiment analysis and natural language inference tasks, classifiers trained on original data fail on their counterfactually-revised counterparts and vice versa. Classifiers trained on combined datasets perform remarkably well, just shy of those specialized to either domain. I will discuss this method, the early results, some conceptual underpinnings of the approach, and recent follow-up work.

Speaker bio: Zachary Chase Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research spans core machine learning methods, applications to clinical medicine and natural language processing, and the impact of automation on social systems. Current research focuses include robustness under distribution shift, decision-making, breast cancer screening, the effective and equitable allocation of organs, and the applications of causal thinking in practical high-dimensional settings that resist stylized causal models. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. Find on Twitter (@zacharylipton) or GitHub (@zackchase), or the internet (acmilab.org).


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