@inproceedings{yousef-etal-2023-evalign,
title = "{EVALIGN}: Visual Evaluation of Translation Alignment Models",
author = {Yousef, Tariq and
Heyer, Gerhard and
J{\"a}nicke, Stefan},
editor = "Croce, Danilo and
Soldaini, Luca",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-demo.31",
doi = "10.18653/v1/2023.eacl-demo.31",
pages = "277--297",
abstract = "This paper presents EvAlign, a visual analytics framework for quantitative and qualitative evaluation of automatic translation alignment models. EvAlign offers various visualization views enabling developers to visualize their models{'} predictions and compare the performance of their models with other baseline and state-of-the-art models. Through different search and filter functions, researchers and practitioners can also inspect the frequent alignment errors and their positions. EvAlign hosts nine gold standard datasets and the predictions of multiple alignment models. The tool is extendable, and adding additional datasets and models is straightforward. EvAlign can be deployed and used locally and is available on GitHub.",
}
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%0 Conference Proceedings
%T EVALIGN: Visual Evaluation of Translation Alignment Models
%A Yousef, Tariq
%A Heyer, Gerhard
%A Jänicke, Stefan
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F yousef-etal-2023-evalign
%X This paper presents EvAlign, a visual analytics framework for quantitative and qualitative evaluation of automatic translation alignment models. EvAlign offers various visualization views enabling developers to visualize their models’ predictions and compare the performance of their models with other baseline and state-of-the-art models. Through different search and filter functions, researchers and practitioners can also inspect the frequent alignment errors and their positions. EvAlign hosts nine gold standard datasets and the predictions of multiple alignment models. The tool is extendable, and adding additional datasets and models is straightforward. EvAlign can be deployed and used locally and is available on GitHub.
%R 10.18653/v1/2023.eacl-demo.31
%U https://aclanthology.org/2023.eacl-demo.31
%U https://doi.org/10.18653/v1/2023.eacl-demo.31
%P 277-297
Markdown (Informal)
[EVALIGN: Visual Evaluation of Translation Alignment Models](https://aclanthology.org/2023.eacl-demo.31) (Yousef et al., EACL 2023)
ACL
- Tariq Yousef, Gerhard Heyer, and Stefan Jänicke. 2023. EVALIGN: Visual Evaluation of Translation Alignment Models. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 277–297, Dubrovnik, Croatia. Association for Computational Linguistics.