130 - Linking human cognitive patterns to NLP Models, with Lisa Beinborn

130 - Linking human cognitive patterns to NLP Models, with Lisa Beinborn

Author: Allen Institute for Artificial Intelligence August 9, 2021 Duration: 44:02
In this episode, we talk with Lisa Beinborn, an assistant professor at Vrije Universiteit Amsterdam, about how to use human cognitive signals to improve and analyze NLP models. We start by discussing different kinds of cognitive signals—eye-tracking, EEG, MEG, and fMRI—and challenges associated with using them. We then turn to Lisa’s recent work connecting interpretability measures with eye-tracking data, which reflect the relative importance measures of different tokens in human reading comprehension. We discuss empirical results suggesting that eye-tracking signals correlate strongly with gradient-based saliency measures, but not attention, in NLP methods. We conclude with discussion of the implications of these findings, as well as avenues for future work. Papers discussed in this episode: Towards best practices for leveraging human language processing signals for natural language processing: https://api.semanticscholar.org/CorpusID:219309655 Relative Importance in Sentence Processing: https://api.semanticscholar.org/CorpusID:235358922 Lisa Beinborn’s webpage: https://beinborn.eu/ The hosts for this episode are Alexis Ross and Pradeep Dasigi.

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: 50

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]

Duration: 43:30
In this episode, Tom Kwiatkowski and Michael Collins talk about Natural Questions, a benchmark for question answering research. We discuss how the dataset was collected to reflect naturally-occurring questions, the crite…
109 - What Does Your Model Know About Language, with Ellie Pavlick [not-audio_url] [/not-audio_url]

Duration: 46:58
How do we know, in a concrete quantitative sense, what a deep learning model knows about language? In this episode, Ellie Pavlick talks about two broad directions to address this question: structural and behavioral analy…
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…