97 - Automated Analysis Of Historical Printed Documents, With Taylor Berg-Kirkpatrick

97 - Automated Analysis Of Historical Printed Documents, With Taylor Berg-Kirkpatrick

Author: Allen Institute for Artificial Intelligence November 28, 2019 Duration: 44:11
In this episode, we talk to Taylor Berg-Kirkpatrick about optical character recognition (OCR) on historical documents. Taylor starts off by describing some practical issues related to old scanning processes of documents that make performing OCR on them a difficult problem. Then he explains how one can build latent variable models for this data using unsupervised methods, the relative importance of various modeling choices, and summarizes how well the models do. We then take a higher level view of historical OCR as a Machine Learning problem, and discuss how it is different from other ML problems in terms of the tradeoff between learning from data and imposing constraints based on prior knowledge of the underlying process. Finally, Taylor talks about the applications of this research, and how these predictions can be of interest to historians studying the original texts.

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
129 - Transformers and Hierarchical Structure, with Shunyu Yao [not-audio_url] [/not-audio_url]

Duration: 35:43
In this episode, we talk to Shunyu Yao about recent insights into how transformers can represent hierarchical structure in language. Bounded-depth hierarchical structure is thought to be a key feature of natural language…
128 - Dynamic Benchmarking, with Douwe Kiela [not-audio_url] [/not-audio_url]

Duration: 47:00
We discussed adversarial dataset construction and dynamic benchmarking in this episode with Douwe Kiela, a research scientist at Facebook AI Research who has been working on a dynamic benchmarking platform called Dynaben…
126 - Optimizing Continuous Prompts for Generation, with Lisa Li [not-audio_url] [/not-audio_url]

Duration: 47:38
We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation. Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-sp…
125 - VQA for Real Users, with Danna Gurari [not-audio_url] [/not-audio_url]

Duration: 42:10
How can we build Visual Question Answering systems for real users? For this episode, we chatted with Danna Gurari, about her work in building datasets and models towards VQA for people who are blind. We talked about the…