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Correct Me If You Can: Learning from Error Corrections and Markings

Correct Me If You Can: Learning from Error Corrections and Markings

23 April 2020
Julia Kreutzer
Nathaniel Berger
Stefan Riezler
ArXiv (abs)PDFHTML

Papers citing "Correct Me If You Can: Learning from Error Corrections and Markings"

10 / 10 papers shown
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling
Zhaopeng Feng
Jiahan Ren
Jiayuan Su
Jiamei Zheng
Hongwei Wang
Hongwei Wang
LRM
519
4
0
15 Mar 2025
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings
Miguel Moura Ramos
Tomás Almeida
Daniel Vareta
Filipe Azevedo
Sweta Agrawal
Patrick Fernandes
Marcely Zanon Boito
501
7
0
08 Nov 2024
Modeling User Preferences with Automatic Metrics: Creating a
  High-Quality Preference Dataset for Machine Translation
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine TranslationConference on Empirical Methods in Natural Language Processing (EMNLP), 2024
Sweta Agrawal
José G. C. de Souza
Ricardo Rei
António Farinhas
Gonçalo Faria
Patrick Fernandes
Nuno M. Guerreiro
Andre Martins
199
9
0
10 Oct 2024
Error Span Annotation: A Balanced Approach for Human Evaluation of
  Machine Translation
Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation
Tom Kocmi
Vilém Zouhar
Eleftherios Avramidis
Roman Grundkiewicz
Marzena Karpinska
Maja Popović
Mrinmaya Sachan
Mariya Shmatova
276
51
0
17 Jun 2024
A Critical Look At Tokenwise Reward-Guided Text Generation
A Critical Look At Tokenwise Reward-Guided Text Generation
Ahmad Rashid
Ruotian Wu
Julia Grosse
Agustinus Kristiadi
Pascal Poupart
OffRL
616
5
0
12 Jun 2024
Prompting Large Language Models with Human Error Markings for
  Self-Correcting Machine Translation
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
Nathaniel Berger
Stefan Riezler
M. Exel
Matthias Huck
LRM
201
2
0
04 Jun 2024
Enhancing Supervised Learning with Contrastive Markings in Neural
  Machine Translation Training
Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation TrainingEuropean Association for Machine Translation Conferences/Workshops (EAMT), 2023
Nathaniel Berger
M. Exel
Matthias Huck
Stefan Riezler
239
2
0
17 Jul 2023
Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement
Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement
Gavin Abercrombie
Tanvi Dinkar
Amanda Cercas Curry
Verena Rieser
Dirk Hovy
268
28
0
25 Jan 2023
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech
  Revolution
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution
Sahan Bulathwela
Maria Perez-Ortiz
C. Holloway
John Shawe-Taylor
207
26
0
03 Dec 2021
How to Evaluate a Summarizer: Study Design and Statistical Analysis for
  Manual Linguistic Quality Evaluation
How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality EvaluationConference of the European Chapter of the Association for Computational Linguistics (EACL), 2021
Julius Steen
K. Markert
ELM
185
14
0
27 Jan 2021
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