Search Results for author:
Found 5 papers, 4 papers with code
Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections.
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners.
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings
We propose ConGraT(Contrastive Graph-Text pretraining), a general, self-supervised method for jointly learning separate representations of texts and nodes in a parent (or ``supervening'') graph, where each text is associated with one of the nodes.
Dubbing in Practice: A Large Scale Study of Human Localization With Insights for Automatic Dubbing
We investigate how humans perform the task of dubbing video content from one language into another, leveraging a novel corpus of 319. 57 hours of video from 54 professionally produced titles.
RadioTalk: a large-scale corpus of talk radio transcripts
We introduce RadioTalk, a corpus of speech recognition transcripts sampled from talk radio broadcasts in the United States between October of 2018 and March of 2019.
