116 - Grounded Language Understanding, with Yonatan Bisk

116 - Grounded Language Understanding, with Yonatan Bisk

Author: Allen Institute for Artificial Intelligence July 3, 2020 Duration: 59:28
We invited Yonatan Bisk to talk about grounded language understanding. We started off by discussing an overview of the topic, its research goals, and the the challenges involved. In the latter half of the conversation, we talked about ALFRED (Shridhar et al., 2019), a grounded instruction following benchmark that simulates training a robot butler. The current best models built for this benchmark perform very poorly compared to humans. We discussed why that might be, and what could be done to improve their performance. Yonatan Bisk is currently an assistant professor at Language Technologies Institute at Carnegie Mellon University. The data and the leaderboard for ALFRED can be accessed here: https://askforalfred.com/.

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
103 - Processing Language in Social Media, with Brendan O'Connor [not-audio_url] [/not-audio_url]

Duration: 43:12
We talked to Brendan O’Connor for this episode about processing language in social media. Brendan started off by telling us about his projects that studied the linguistic and geographical patterns of African American Eng…
101 - The lottery ticket hypothesis, with Jonathan Frankle [not-audio_url] [/not-audio_url]

Duration: 41:16
In this episode, Jonathan Frankle describes the lottery ticket hypothesis, a popular explanation of how over-parameterization helps in training neural networks. We discuss pruning methods used to uncover subnetworks (win…
100 - NLP Startups, with Oren Etzioni [not-audio_url] [/not-audio_url]

Duration: 30:55
For our 100th episode, we invite AI2 CEO Oren Etzioni to talk to us about NLP startups. Oren has founded several successful startups, is himself an investor in startups, and helps with AI2's startup incubator. Some of ou…
98 - Analyzing Information Flow In Transformers, With Elena Voita [not-audio_url] [/not-audio_url]

Duration: 37:05
What function do the different attention heads serve in multi-headed attention models? In this episode, Lena describes how to use attribution methods to assess the importance and contribution of different heads in severa…
95 - Common sense reasoning, with Yejin Choi [not-audio_url] [/not-audio_url]

Duration: 35:29
In this episode, we invite Yejin Choi to talk about common sense knowledge and reasoning, a growing area in NLP. We start by discussing a working definition of “common sense” and the practical utility of studying it. We…
94 - Decompositional Semantics, with Aaron White [not-audio_url] [/not-audio_url]

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 co…