1 |
Towards Explainable Evaluation Metrics for Natural Language Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Pushing the right buttons: adversarial evaluation of quality estimation
|
|
|
|
In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
|
|
BASE
|
|
Show details
|
|
5 |
Continual Quality Estimation with Online Bayesian Meta-Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Findings of the WMT 2021 Shared Task on Quality Estimation ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
deepQuest-py: large and distilled models for quality estimation
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Findings of the WMT 2021 shared task on quality estimation
|
|
|
|
In: 689 ; 730 (2021)
|
|
BASE
|
|
Show details
|
|
12 |
deepQuest-py: large and distilled models for quality estimation
|
|
|
|
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations ; 382 ; 389 (2021)
|
|
BASE
|
|
Show details
|
|
13 |
Backtranslation feedback improves user confidence in MT, not quality
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Knowledge distillation for quality estimation
|
|
|
|
In: 5091 ; 5099 (2021)
|
|
BASE
|
|
Show details
|
|
15 |
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Unsupervised quality estimation for neural machine translation
|
|
|
|
In: 8 ; 539 ; 555 (2020)
|
|
BASE
|
|
Show details
|
|
17 |
An exploratory study on multilingual quality estimation
|
|
|
|
In: 366 ; 377 (2020)
|
|
BASE
|
|
Show details
|
|
18 |
BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
|
|
|
|
In: 1010 ; 1017 (2020)
|
|
BASE
|
|
Show details
|
|
19 |
Findings of the WMT 2020 shared task on quality estimation
|
|
|
|
In: 743 ; 764 (2020)
|
|
Abstract:
© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website; https://www.aclweb.org/anthology/2020.wmt-1.79 ; We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels. This edition included new data with open domain texts, direct assessment annotations, and multiple language pairs: English-German, English-Chinese, Russian-English, Romanian-English, Estonian-English, Sinhala-English and Nepali-English data for the sentence-level subtasks, English-German and English-Chinese for the word-level subtask, and English-French data for the document-level subtask. In addition, we made neural machine translation models available to participants. 19 participating teams from 27 institutions submitted altogether 1374 systems to different task variants and language pairs.
|
|
URL: http://hdl.handle.net/2436/623855
|
|
BASE
|
|
Hide details
|
|
20 |
MLQE-PE: A multilingual quality estimation and post-editing dataset
|
|
|
|
BASE
|
|
Show details
|
|
|
|