@inproceedings{castilho-etal-2023-online,
title = "Do online Machine Translation Systems Care for Context? What About a {GPT} Model?",
author = "Castilho, Sheila and
Mallon, Clodagh Quinn and
Meister, Rahel and
Yue, Shengya",
editor = "Nurminen, Mary and
Brenner, Judith and
Koponen, Maarit and
Latomaa, Sirkku and
Mikhailov, Mikhail and
Schierl, Frederike and
Ranasinghe, Tharindu and
Vanmassenhove, Eva and
Vidal, Sergi Alvarez and
Aranberri, Nora and
Nunziatini, Mara and
Escart{\'\i}n, Carla Parra and
Forcada, Mikel and
Popovic, Maja and
Scarton, Carolina and
Moniz, Helena",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.39",
pages = "393--417",
abstract = "This paper addresses the challenges of evaluating document-level machine translation (MT) in the context of recent advances in context-aware neural machine translation (NMT). It investigates how well online MT systems deal with six context-related issues, namely lexical ambiguity, grammatical gender, grammatical number, reference, ellipsis, and terminology, when a larger context span containing the solution for those issues is given as input. Results are compared to the translation outputs from the online ChatGPT. Our results show that, while the change of punctuation in the input yields great variability in the output translations, the context position does not seem to have a great impact. Moreover, the GPT model seems to outperform the NMT systems but performs poorly for Irish. The study aims to provide insights into the effectiveness of online MT systems in handling context and highlight the importance of considering contextual factors in evaluating MT systems.",
}
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<abstract>This paper addresses the challenges of evaluating document-level machine translation (MT) in the context of recent advances in context-aware neural machine translation (NMT). It investigates how well online MT systems deal with six context-related issues, namely lexical ambiguity, grammatical gender, grammatical number, reference, ellipsis, and terminology, when a larger context span containing the solution for those issues is given as input. Results are compared to the translation outputs from the online ChatGPT. Our results show that, while the change of punctuation in the input yields great variability in the output translations, the context position does not seem to have a great impact. Moreover, the GPT model seems to outperform the NMT systems but performs poorly for Irish. The study aims to provide insights into the effectiveness of online MT systems in handling context and highlight the importance of considering contextual factors in evaluating MT systems.</abstract>
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%0 Conference Proceedings
%T Do online Machine Translation Systems Care for Context? What About a GPT Model?
%A Castilho, Sheila
%A Mallon, Clodagh Quinn
%A Meister, Rahel
%A Yue, Shengya
%Y Nurminen, Mary
%Y Brenner, Judith
%Y Koponen, Maarit
%Y Latomaa, Sirkku
%Y Mikhailov, Mikhail
%Y Schierl, Frederike
%Y Ranasinghe, Tharindu
%Y Vanmassenhove, Eva
%Y Vidal, Sergi Alvarez
%Y Aranberri, Nora
%Y Nunziatini, Mara
%Y Escartín, Carla Parra
%Y Forcada, Mikel
%Y Popovic, Maja
%Y Scarton, Carolina
%Y Moniz, Helena
%S Proceedings of the 24th Annual Conference of the European Association for Machine Translation
%D 2023
%8 June
%I European Association for Machine Translation
%C Tampere, Finland
%F castilho-etal-2023-online
%X This paper addresses the challenges of evaluating document-level machine translation (MT) in the context of recent advances in context-aware neural machine translation (NMT). It investigates how well online MT systems deal with six context-related issues, namely lexical ambiguity, grammatical gender, grammatical number, reference, ellipsis, and terminology, when a larger context span containing the solution for those issues is given as input. Results are compared to the translation outputs from the online ChatGPT. Our results show that, while the change of punctuation in the input yields great variability in the output translations, the context position does not seem to have a great impact. Moreover, the GPT model seems to outperform the NMT systems but performs poorly for Irish. The study aims to provide insights into the effectiveness of online MT systems in handling context and highlight the importance of considering contextual factors in evaluating MT systems.
%U https://aclanthology.org/2023.eamt-1.39
%P 393-417
Markdown (Informal)
[Do online Machine Translation Systems Care for Context? What About a GPT Model?](https://aclanthology.org/2023.eamt-1.39) (Castilho et al., EAMT 2023)
ACL