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

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

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Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. All views expressed belong to the hosts/guests, and do not represent their employers.

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

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