54 - Simulating Action Dynamics with Neural Process Networks, with Antoine Bosselut

54 - Simulating Action Dynamics with Neural Process Networks, with Antoine Bosselut

Author: Allen Institute for Artificial Intelligence March 26, 2018 Duration: 36:04
ICLR 2018 paper, by Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, and Yejin Choi. This is not your standard NLP task. This work tries to predict which entities change state over the course of a recipe (e.g., ingredients get combined into a batter, so entities merge, and then the batter gets baked, changing location, temperature, and "cookedness"). We talk to Antoine about the work, getting into details about how the data was collected, how the model works, and what some possible future directions are. https://www.semanticscholar.org/paper/Simulating-Action-Dynamics-with-Neural-Process-Bosselut-Levy/dc01c9401d1caab7f5e6d2f1280f5815f6919977

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
Podcast Episodes
74 - Deep Reinforcement Learning Doesn't Work Yet, with Alex Irpan [not-audio_url] [/not-audio_url]

Duration: 40:43
Blog post by Alex Irpan titled "Deep Reinforcement Learning Doesn't Work Yet" https://www.alexirpan.com/2018/02/14/rl-hard.html In this episode, Alex Irpan talks about limitations of current deep reinforcement learning m…
72 - The Anatomy Question Answering Task, with Jordan Boyd-Graber [not-audio_url] [/not-audio_url]

Duration: 43:14
Our first episode in a new format: broader surveys of areas, instead of specific discussions on individual papers. In this episode, we talk with Jordan Boyd-Graber about question answering. Matt starts the discussion by…
69 - Second language acquisition modeling, with Burr Settles [not-audio_url] [/not-audio_url]

Duration: 34:55
A shared task held in conjunction with a NAACL 2018 workshop, organized by Burr Settles and collaborators at Duolingo. Burr tells us about the shared task. The goal of the task was to predict errors that a language learn…
68 - Neural models of factuality, with Rachel Rudinger [not-audio_url] [/not-audio_url]

Duration: 36:57
NAACL 2018 paper, by Rachel Rudinger, Aaron Steven White, and Benjamin Van Durme Rachel comes on to the podcast, telling us about what factuality is (did an event happen?), what datasets exist for doing this task (a few;…