1 citations · 2 across the 4 of their papers we have counts for
8 papers
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…
Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization
Mayumi Ohta, Nathaniel Berger, Artem Sokolov +1
Interest in stochastic zeroth-order (SZO) methods has recently been revived in black-box optimization scenarios such as adversarial black-box attacks to deep neural networks. SZO m…
Learning to Segment Inputs for NMT Favors Character-Level Processing
Julia Kreutzer, Artem Sokolov
Most modern neural machine translation (NMT) systems rely on presegmented inputs. Segmentation granularity importantly determines the input and output sequence lengths, hence the m…
Sparse Stochastic Zeroth-Order Optimization with an Application to Bandit Structured Prediction
Artem Sokolov, Julian Hitschler, Mayumi Ohta +1
Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however,…