46 - Parsing with Traces, with Jonathan Kummerfeld

46 - Parsing with Traces, with Jonathan Kummerfeld

Author: Allen Institute for Artificial Intelligence January 9, 2018 Duration: 39:11
TACL 2017 paper by Jonathan K. Kummerfeld and Dan Klein. Jonathan tells us about his work on parsing algorithms that capture traces and null elements in sentence structure. We spend the first third of the conversation talking about what these are and why they are interesting - if you want to correctly handle wh-movement, or coordinating structures, or control structures, or many other phenomena that we commonly see in language, you really want to handle traces and null elements, but most current parsers totally ignore these phenomena. The second third of the conversation is about how the parser works, and we conclude by talking about some of the implications of the work, and where to go next - should we really be pushing harder on capturing linguistic structure when everyone seems to be going towards end-to-end learning on some higher-level task? https://www.semanticscholar.org/paper/Parsing-with-Traces-An-O-n-4-Algorithm-and-a-Struc-Kummerfeld-Klein/af89e56b3d9b720d43cae9f4971928c5cb95cbe3 Jonathan also blogs about papers that he's reading; check out his paper summaries at http://jkk.name/

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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