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InterNLP
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2026-02-24 09:18:45
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InterNLP Home Organization Background & References Past Workshops Workshop on Interactive Learning for Natural Language Processing Contact the organizers: internlp2022@googlegroups.com Motivation Interactive machine learning studies algorithms that learn from data collected through interaction with a computational agent or human 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. 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. Goals We...
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