94 - Decompositional Semantics, with Aaron White

94 - Decompositional Semantics, with Aaron White

Author: Allen Institute for Artificial Intelligence October 1, 2019 Duration: 27:50
In this episode, Aaron White tells us about the decompositional semantics initiative (Decomp), an attempt to re-think the prototypical approach to semantic representation and annotation. The basic idea is to decompose complex semantic classes such as ‘agent’ and ‘patient’ into simpler semantic properties such as ‘causation’ and ‘volition’, while embracing the uncertainty inherent in language by allowing annotators to choose answers such as ‘probably’ or ‘probably not’. In order to scale the collection of labeled data, each property is annotated by asking crowd workers intuitive questions about phrases in a given sentence. Aaron White's homepage: http://aaronstevenwhite.io/ Decomp initiative page: http://decomp.io/

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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