92 - Computational Humanities, with David Bamman

92 - Computational Humanities, with David Bamman

Author: Allen Institute for Artificial Intelligence July 5, 2019 Duration: 33:56
In this episode, we invite David Bamman to give an overview of computational humanities. We discuss examples of questions studied in computational humanities (e.g., characterizing fictionality, assessing novelty, measuring the attention given to male vs. female characters in the literature). We talk about the role NLP plays in addressing these questions and how the accuracy and biases of NLP models can influence the results. We also discuss understudied NLP tasks which can help us answer more questions in this domain such as literary scene coreference resolution and constructing a map of literature geography. David Bamman's homepage: http://people.ischool.berkeley.edu/~dbamman/ LitBank dataset: https://github.com/dbamman/litbank

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
83 - Knowledge Base Construction, with Sebastian Riedel [not-audio_url] [/not-audio_url]

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80 - Leaderboards and Science, with Siva Reddy [not-audio_url] [/not-audio_url]

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Originally used to entice fierce competitions in arcade games, leaderboards recently made their way into NLP research circles. Leaderboards could help mitigate some of the problems in how researchers run experiments and…
79 - The glass ceiling in NLP, with Natalie Schluter [not-audio_url] [/not-audio_url]

Duration: 26:31
In this episode, Natalie Schluter talks to us about a data-driven analysis of career progression of male vs. female researchers in NLP through the lens of mentor-mentee networks based on ~20K papers in the ACL anthology.…
78. Where do corpora come from?, with Matt Honnibal and Ines Montani [not-audio_url] [/not-audio_url]

Duration: 30:21
Most NLP projects rely crucially on the quality of annotations used for training and evaluating models. In this episode, Matt and Ines of Explosion AI tell us how Prodigy can improve data annotation and model development…
77. On Writing Quality Peer Reviews, with Noah A. Smith [not-audio_url] [/not-audio_url]

Duration: 38:12
It's not uncommon for authors to be frustrated with the quality of peer reviews they receive in (NLP) conferences. In this episode, Noah A. Smith shares his advice on how to write good peer reviews. The structure Noah re…
75 - Reinforcement / Imitation Learning in NLP, with Hal Daumé III [not-audio_url] [/not-audio_url]

Duration: 43:54
In this episode, we invite Hal Daumé to continue the discussion on reinforcement learning, focusing on how it has been used in NLP. We discuss how to reduce NLP problems into the reinforcement learning framework, and cir…
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…