135 - PhD Application Series: After Submitting Applications

135 - PhD Application Series: After Submitting Applications

Author: Allen Institute for Artificial Intelligence March 3, 2022 Duration: 36:53
This episode is the third in our current series on PhD applications. We talk about what the PhD application process looks like after applications are submitted. We start with a general overview of the timeline, then talk about how to approach interviews and conversations with faculty, and finish by discussing the different factors to consider in deciding between programs. The guests for this episode are Rada Mihalcea (Professor at the University of Michigan), Aishwarya Kamath (PhD student at NYU), and Sanjay Subramanian (PhD student at UC Berkeley). Homepages: - Aishwarya Kamath: https://ashkamath.github.io/ - Sanjay Subramanian: https://sanjayss34.github.io/ - Rada Mihalcea: https://web.eecs.umich.edu/~mihalcea/ The hosts for this episode are Alexis Ross and Nishant Subramani.

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]

Duration: 38:21
In this episode, we invite Sebastian Riedel to talk about knowledge base construction (KBC). Why is it an important research area? What are the tradeoffs between using an open vs. closed schema? What are popular methods…
82 - Visual Reasoning, with Yoav Artzi [not-audio_url] [/not-audio_url]

Duration: 42:22
In this episode, Yoav Artzi joins us to talk about visual reasoning. We start by defining what visual reasoning is, then discuss the pros and cons of different tasks and datasets. We discuss some of the models used for v…
81 - BlackboxNLP, with Afra Alishahi and Tal Linzen [not-audio_url] [/not-audio_url]

Duration: 31:01
Neural models recently resulted in large performance improvements in various NLP problems, but our understanding of what and how the models learn remains fairly limited. In this episode, Tal Linzen and Afra Alishahi talk…
80 - Leaderboards and Science, with Siva Reddy [not-audio_url] [/not-audio_url]

Duration: 29:48
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…