65 - Event Representations with Tensor-based Compositions, with Niranjan Balasubramanian

65 - Event Representations with Tensor-based Compositions, with Niranjan Balasubramanian

Author: Allen Institute for Artificial Intelligence August 14, 2018 Duration: 38:38
AAAI 2018 paper by Noah Weber, Niranjan Balasubramanian, and Nathanael Chambers Niranjan joins us on the podcast to tell us about his latest contribution in a line of work going back to Shank's scripts. This work tries to model sequences of events to get coherent narrative schemas, mined from large collections of text. For example, given an event like "She threw a football", you might expect future events involving catching, running, scoring, and so on. But if the event is instead "She threw a bomb", you would expect future events to involve things like explosions, damage, arrests, or other related things. We spend much of our conversation talking about why these scripts are interesting to study, and the general outline for how one might learn these scripts from text, and spend a little bit of time talking about the particular contribution of this paper, which is a better model that captures interactions among all of the arguments to an event. https://www.semanticscholar.org/paper/Event-Representations-With-Tensor-Based-Weber-Balasubramanian/418f405a60b8d9009099777f7ae37f4496542f90

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
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