48 - Incidental Supervision: Moving Beyond Supervised Learning, with Dan Roth

48 - Incidental Supervision: Moving Beyond Supervised Learning, with Dan Roth

Author: Allen Institute for Artificial Intelligence January 29, 2018 Duration: 27:52
AAAI 2017 paper, by Dan Roth. In this episode we have a conversation with Dan about what he means by "incidental supervision", and how it's related to ideas in reinforcement learning and representation learning. For many tasks, there are signals you can get from seemingly unrelated data that will help you in making predictions. Leveraging the international news cycle to learn transliteration models for named entities is one example of this, as is the current trend in NLP of using language models or other multi-task signals to do better representation learning for your end task. Dan argues that we need to be thinking about this more explicitly in our research, instead of learning everything "end-to-end", as we will never have enough data to learn complex tasks directly from annotations alone. https://www.semanticscholar.org/paper/Incidental-Supervision-Moving-beyond-Supervised-Le-Roth/2997dcfc6d5ffc262d57d0a26f74d091de096573

While NLP Highlights is currently on hiatus, its archive remains a compelling snapshot of conversations from the front lines of computational linguistics. Produced by the Allen Institute for Artificial Intelligence, this science podcast carved out a space for deep, researcher-led discussions about natural language processing. Each episode functions as an informal seminar, where the people actively designing algorithms and pushing the field forward explain their work in their own words. You'll hear about the nuanced challenges behind making machines understand, generate, and reason with human language, from foundational theories to unexpected applications. The dialogue in this podcast often delves into the "why" behind the research, not just the results, offering clarity on complex topics like machine translation, sentiment analysis, or large language models. It’s a chance to listen as experts articulate their thought processes, debates, and moments of insight, with all the candidness that comes from a conversation between peers. The views shared are personal perspectives from the hosts and their guests, independent of their affiliated institutions. For anyone curious about how machines learn to parse meaning, the archived episodes of NLP Highlights provide a thoughtful and accessible entry point.
Author: Language: English Episodes: 100

NLP Highlights
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