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InterNLP: Workshop on Interactive Learning for Natural Language Processing
Kianté Brantley · Soham Dan · Ji Ung Lee · Khanh Nguyen · Edwin Simpson · Alane Suhr · Yoav Artzi

Sat Dec 03 07:00 AM -- 02:55 PM (PST) @ Room 397
Event URL: https://internlp.github.io »

Interactive machine learning studies algorithms that learn from data collected through interaction with either a computational or human agent in a shared environment, through feedback on model decisions. In contrast to the common paradigm of supervised learning, IML does not assume access to pre-collected labeled data, thereby decreasing data costs. Instead, it allows systems to improve over time, empowering non-expert users to provide feedback. IML has seen wide success in areas such as video games and recommendation systems.
Although most downstream applications of NLP involve interactions with humans - e.g., via labels, demonstrations, corrections, or evaluation - common NLP models are not built to learn from or adapt to users through interaction. There remains a large research gap that must be closed to enable NLP systems that adapt on-the-fly to the changing needs of humans and dynamic environments through interaction.

Author Information

Kianté Brantley (The University of Maryland College Park)
Soham Dan (University of Pennsylvania)

PhD student at the University of Pennsylvania advised by Prof. Dan Roth working on machine learning and natural language processing.

Ji Ung Lee (UKP, TU Darmstadt)
Khanh Nguyen (Princeton University)
Edwin Simpson (University of Bristol)
Edwin Simpson

I am a lecturer (assistant professor) at the University of Bristol, specialising in interactive machine learning for NLP and learning from crowdsourced data. Previously, I was a postdoc at TU Darmstadt, Germany, and completed my doctorate at the University of Oxford. Talk to me about uncertainty and Bayesian methods in natural language processing, learning from instructions, combining classifications, and text summarisation.

Alane Suhr (AI2)
Yoav Artzi (Cornell University)

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