187 citations · 423 across the 18 of their papers we have counts for
12 papers · 1 filter
Training and Evaluation of Guideline-Based Medical Reasoning in LLMs
Michael Staniek, Artem Sokolov, Stefan Riezler
Machine learning for early prediction in medicine has recently shown breakthrough performance, however, the focus on improving prediction accuracy has led to a neglect of faithful…
Improving End-to-End Speech Translation by Imitation-Based Knowledge Distillation with Synthetic Transcripts
Rebekka Hubert, Artem Sokolov, Stefan Riezler
End-to-end automatic speech translation (AST) relies on data that combines audio inputs with text translation outputs. Previous work used existing large parallel corpora of transcr…
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets
Irina Bejan, Artem Sokolov, Katja Filippova
Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art in NLP. However, data quality might have already become the bottleneck to unlock fu…
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…
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…
Sockeye: A Toolkit for Neural Machine Translation
Felix Hieber, Tobias Domhan, Michael Denkowski +4
We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and apply…
A Shared Task on Bandit Learning for Machine Translation
Artem Sokolov, Julia Kreutzer, Kellen Sunderland +4
We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the…
Stochastic Structured Prediction under Bandit Feedback
Artem Sokolov, Julia Kreutzer, Christopher Lo +1
Stochastic structured prediction under bandit feedback follows a learning protocol where on each of a sequence of iterations, the learner receives an input, predicts an output stru…
Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation
Artem Sokolov, Stefan Riezler, Tanguy Urvoy
We present an approach to structured prediction from bandit feedback, called Bandit Structured Prediction, where only the value of a task loss function at a single predicted point,…