Search Results for author: Stefan Riezler

Found 65 papers, 20 papers with code

Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs

no code implementations EMNLP (IWSLT) 2019 Philipp Wiesenbach, Stefan Riezler

Machine translation of ancient languages faces a low-resource problem, caused by the limited amount of available textual source data and their translations.

Machine Translation Multi-Task Learning +5

Validity problems in clinical machine learning by indirect data labeling using consensus definitions

1 code implementation6 Nov 2023 Michael Hagmann, Shigehiko Schamoni, Stefan Riezler

We demonstrate a validity problem of machine learning in the vital application area of disease diagnosis in medicine.

Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap

1 code implementation30 Aug 2023 Michael Staniek, Raphael Schumann, Maike Züfle, Stefan Riezler

We present Text-to-OverpassQL, a task designed to facilitate a natural language interface for querying geodata from OpenStreetMap (OSM).

Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training

no code implementations17 Jul 2023 Nathaniel Berger, Miriam Exel, Matthias Huck, Stefan Riezler

Supervised learning in Neural Machine Translation (NMT) typically follows a teacher forcing paradigm where reference tokens constitute the conditioning context in the model's prediction, instead of its own previous predictions.

Machine Translation NMT +1

Improving End-to-End Speech Translation by Imitation-Based Knowledge Distillation with Synthetic Transcripts

1 code implementation17 Jul 2023 Rebekka Hubert, Artem Sokolov, Stefan Riezler

We present an imitation learning approach where a teacher NMT system corrects the errors of an AST student without relying on manual transcripts.

Imitation Learning Knowledge Distillation +4

VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View

1 code implementation12 Jul 2023 Raphael Schumann, Wanrong Zhu, Weixi Feng, Tsu-Jui Fu, Stefan Riezler, William Yang Wang

In this work, we propose VELMA, an embodied LLM agent that uses a verbalization of the trajectory and of visual environment observations as contextual prompt for the next action.

Decision Making Natural Language Understanding +1

Towards Inferential Reproducibility of Machine Learning Research

no code implementations8 Feb 2023 Michael Hagmann, Philipp Meier, Stefan Riezler

Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models.

JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT

2 code implementations5 Oct 2022 Mayumi Ohta, Julia Kreutzer, Stefan Riezler

JoeyS2T is a JoeyNMT extension for speech-to-text tasks such as automatic speech recognition and end-to-end speech translation.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis

1 code implementation1 Sep 2022 Shigehiko Schamoni, Michael Hagmann, Stefan Riezler

Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee decision.

Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks

no code implementations16 Sep 2021 Nathaniel Berger, Stefan Riezler, Artem Sokolov, Sebastian Ebert

Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP).

Adversarial Text

False perfection in machine prediction: Detecting and assessing circularity problems in machine learning

no code implementations23 Jun 2021 Michael Hagmann, Stefan Riezler

This paper is an excerpt of an early version of Chapter 2 of the book "Validity, Reliability, and Significance.

BIG-bench Machine Learning

Error-Aware Interactive Semantic Parsing of OpenStreetMap

no code implementations ACL (splurobonlp) 2021 Michael Staniek, Stefan Riezler

In semantic parsing of geographical queries against real-world databases such as OpenStreetMap (OSM), unique correct answers do not necessarily exist.

Semantic Parsing

On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR

1 code implementation3 Apr 2021 Tsz Kin Lam, Mayumi Ohta, Shigehiko Schamoni, Stefan Riezler

Our method, called Aligned Data Augmentation (ADA) for ASR, replaces transcribed tokens and the speech representations in an aligned manner to generate previously unseen training pairs.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem

no code implementations ACL 2021 Raphael Schumann, Stefan Riezler

Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks.

Natural Language Landmark Navigation Instructions Generation

Embedding Meta-Textual Information for Improved Learning to Rank

no code implementations COLING 2020 Toshitaka Kuwa, Shigehiko Schamoni, Stefan Riezler

Neural approaches to learning term embeddings have led to improved computation of similarity and ranking in information retrieval (IR).

Information Retrieval Learning-To-Rank +2

Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization

1 code implementation2 Jun 2020 Mayumi Ohta, Nathaniel Berger, Artem Sokolov, Stefan Riezler

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.

Joey NMT: A Minimalist NMT Toolkit for Novices

8 code implementations IJCNLP 2019 Julia Kreutzer, Jasmijn Bastings, Stefan Riezler

We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices.

General Knowledge Machine Translation +2

Self-Regulated Interactive Sequence-to-Sequence Learning

7 code implementations ACL 2019 Julia Kreutzer, Stefan Riezler

Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning.

Active Learning Machine Translation +1

Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss

1 code implementation TACL 2019 Laura Jehl, Carolin Lawrence, Stefan Riezler

We show that bipolar ramp loss objectives outperform other non-bipolar ramp loss objectives and minimum risk training (MRT) on both weakly supervised tasks, as well as on a supervised machine translation task.

Machine Translation Semantic Parsing +1

Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation

no code implementations WS 2019 Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler

We propose an interactive-predictive neural machine translation framework for easier model personalization using reinforcement and imitation learning.

Imitation Learning Machine Translation +1

Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing

1 code implementation29 Nov 2018 Carolin Lawrence, Stefan Riezler

In semantic parsing for question-answering, it is often too expensive to collect gold parses or even gold answers as supervision signals.

counterfactual Question Answering +1

Sparse Stochastic Zeroth-Order Optimization with an Application to Bandit Structured Prediction

no code implementations12 Jun 2018 Artem Sokolov, Julian Hitschler, Mayumi Ohta, Stefan Riezler

Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however, the iteration complexity of SZO methods suffers a factor proportional to the dimensionality of the perturbed function.

Structured Prediction

Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning

1 code implementation ACL 2018 Julia Kreutzer, Joshua Uyheng, Stefan Riezler

We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT).

Machine Translation NMT +3

A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation

1 code implementation3 May 2018 Tsz Kin Lam, Julia Kreutzer, Stefan Riezler

We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations.

Machine Translation reinforcement-learning +2

Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback

1 code implementation ACL 2018 Carolin Lawrence, Stefan Riezler

Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system.

counterfactual Semantic Parsing

Can Neural Machine Translation be Improved with User Feedback?

no code implementations NAACL 2018 Julia Kreutzer, Shahram Khadivi, Evgeny Matusov, Stefan Riezler

We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform.

Machine Translation NMT +1

A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits

no code implementations13 Dec 2017 Sariya Karimova, Patrick Simianer, Stefan Riezler

The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language.

Machine Translation NMT +1

Counterfactual Learning for Machine Translation: Degeneracies and Solutions

no code implementations23 Nov 2017 Carolin Lawrence, Pratik Gajane, Stefan Riezler

Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions.

counterfactual Machine Translation +1

Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation

no code implementations EMNLP 2017 Carolin Lawrence, Artem Sokolov, Stefan Riezler

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system.

counterfactual Machine Translation +2

Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation

no code implementations28 Jul 2017 Carolin Lawrence, Artem Sokolov, Stefan Riezler

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system.

counterfactual Machine Translation +1

Bandit Structured Prediction for Neural Sequence-to-Sequence Learning

1 code implementation ACL 2017 Julia Kreutzer, Artem Sokolov, Stefan Riezler

Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback.

Domain Adaptation Machine Translation +3

NLmaps: A Natural Language Interface to Query OpenStreetMap

no code implementations COLING 2016 Carolin Lawrence, Stefan Riezler

We present a Natural Language Interface (nlmaps. cl. uni-heidelberg. de) to query OpenStreetMap.

A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation

no code implementations COLING 2016 Patrick Simianer, Sariya Karimova, Stefan Riezler

Our translation systems may learn from post-edits using several weight, language model and novel translation model adaptation techniques, in part by exploiting the output of the graphical interface.

Domain Adaptation Language Modelling +2

Learning to translate from graded and negative relevance information

no code implementations COLING 2016 Laura Jehl, Stefan Riezler

We present an approach for learning to translate by exploiting cross-lingual link structure in multilingual document collections.

Language Modelling Translation

Stochastic Structured Prediction under Bandit Feedback

1 code implementation NeurIPS 2016 Artem Sokolov, Julia Kreutzer, Christopher Lo, Stefan Riezler

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 structure, and receives partial feedback in form of a task loss evaluation of the predicted structure.

Structured Prediction

Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation

no code implementations18 Jan 2016 Artem Sokolov, Stefan Riezler, Tanguy Urvoy

We present an application to discriminative reranking in Statistical Machine Translation (SMT) where the learning algorithm only has access to a 1-BLEU loss evaluation of a predicted translation instead of obtaining a gold standard reference translation.

Machine Translation Structured Prediction +1

Multimodal Pivots for Image Caption Translation

no code implementations ACL 2016 Julian Hitschler, Shigehiko Schamoni, Stefan Riezler

We present an approach to improve statistical machine translation of image descriptions by multimodal pivots defined in visual space.

Image Retrieval Machine Translation +2

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