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
113 - Managing Industry Research Teams, with Fernando Pereira [not-audio_url] [/not-audio_url]

Duration: 42:22
We invited Fernando Pereira, a VP and Distinguished Engineer at Google, where he leads NLU and ML research, to talk about managing NLP research teams in industry. Topics we discussed include prioritizing research against…
110 - Natural Questions, with Tom Kwiatkowski and Michael Collins [not-audio_url] [/not-audio_url]

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109 - What Does Your Model Know About Language, with Ellie Pavlick [not-audio_url] [/not-audio_url]

Duration: 46:58
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107 - Multi-Modal Transformers, with Hao Tan and Mohit Bansal [not-audio_url] [/not-audio_url]

Duration: 37:34
In this episode, we invite Hao Tan and Mohit Bansal to talk about multi-modal training of transformers, focusing in particular on their EMNLP 2019 paper that introduced LXMERT, a vision+language transformer. We spend the…
106 - Ethical Considerations In NLP Research, with Emily Bender [not-audio_url] [/not-audio_url]

Duration: 39:18
In this episode, we talked to Emily Bender about the ethical considerations in developing NLP models and putting them in production. Emily cited specific examples of ethical issues, and talked about the kinds of potentia…
105 - Question Generation, with Sudha Rao [not-audio_url] [/not-audio_url]

Duration: 42:59
In this episode we invite Sudha Rao to talk about question generation. We talk about different settings where you might want to generate questions: for human testing scenarios (rare), for data augmentation (has been done…
104 - Model Distillation, with Victor Sanh and Thomas Wolf [not-audio_url] [/not-audio_url]

Duration: 31:22
In this episode we talked with Victor Sanh and Thomas Wolf from HuggingFace about model distillation, and DistilBERT as one example of distillation. The idea behind model distillation is compressing a large model by buil…