187 citations · 423 across the 18 of their papers we have counts for
5 papers · 1 filter
Scaling Up Influence Functions
Andrea Schioppa, Polina Zablotskaia, David Vilar +1
We address efficient calculation of influence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hessian…
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
Julia Kreutzer, David Vilar, Artem Sokolov
Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or di…
Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks
Nathaniel Berger, Stefan Riezler, Artem Sokolov +1
Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of searc…
Fixing exposure bias with imitation learning needs powerful oracles
Luca Hormann, Artem Sokolov
We apply imitation learning (IL) to tackle the NMT exposure bias problem with error-correcting oracles, and evaluate an SMT lattice-based oracle which, despite its excellent perfor…
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
Julia Kreutzer, Isaac Caswell, Lisa Wang +49
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text dataset…