@inproceedings{menezes-etal-2022-case,
title = "A Case Study on the Importance of Named Entities in a Machine Translation Pipeline for Customer Support Content",
author = "Menezes, Miguel and
Cabarrão, Vera and
Mota, Pedro and
{Helena Moniz} and
Lavie, Alon",
editor = {Moniz, Helena and
Macken, Lieve and
Rufener, Andrew and
Barrault, Lo{\"\i}c and
Costa-juss{\`a}, Marta R. and
Declercq, Christophe and
Koponen, Maarit and
Kemp, Ellie and
Pilos, Spyridon and
Forcada, Mikel L. and
Scarton, Carolina and
Van den Bogaert, Joachim and
Daems, Joke and
Tezcan, Arda and
Vanroy, Bram and
Fonteyne, Margot},
booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2022",
address = "Ghent, Belgium",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2022.eamt-1.24",
pages = "211--219",
abstract = "This paper describes the research developed at Unbabel, a Portuguese Machine-translation start-up, that combines MT with human post-edition and focuses strictly on customer service content. We aim to contribute to furthering MT quality and good-practices by exposing the importance of having a continuously-in-development robust Named Entity Recognition system compliant with General Data Protection Regulation (GDPR). Moreover, we have tested semiautomatic strategies that support and enhance the creation of Named Entities gold standards to allow a more seamless implementation of Multilingual Named Entities Recognition Systems. The project described in this paper is the result of a shared work between Unbabel ́s linguists and Unbabel ́s AI engineering team, matured over a year. The project should, also, be taken as a statement of multidisciplinary, proving and validating the much-needed articulation between the different scientific fields that compose and characterize the area of Natural Language Processing (NLP).",
}
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%0 Conference Proceedings
%T A Case Study on the Importance of Named Entities in a Machine Translation Pipeline for Customer Support Content
%A Menezes, Miguel
%A Cabarrão, Vera
%A Mota, Pedro
%A Lavie, Alon
%Y Moniz, Helena
%Y Macken, Lieve
%Y Rufener, Andrew
%Y Barrault, Loïc
%Y Costa-jussà, Marta R.
%Y Declercq, Christophe
%Y Koponen, Maarit
%Y Kemp, Ellie
%Y Pilos, Spyridon
%Y Forcada, Mikel L.
%Y Scarton, Carolina
%Y Van den Bogaert, Joachim
%Y Daems, Joke
%Y Tezcan, Arda
%Y Vanroy, Bram
%Y Fonteyne, Margot
%A Helena Moniz
%S Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
%D 2022
%8 June
%I European Association for Machine Translation
%C Ghent, Belgium
%F menezes-etal-2022-case
%X This paper describes the research developed at Unbabel, a Portuguese Machine-translation start-up, that combines MT with human post-edition and focuses strictly on customer service content. We aim to contribute to furthering MT quality and good-practices by exposing the importance of having a continuously-in-development robust Named Entity Recognition system compliant with General Data Protection Regulation (GDPR). Moreover, we have tested semiautomatic strategies that support and enhance the creation of Named Entities gold standards to allow a more seamless implementation of Multilingual Named Entities Recognition Systems. The project described in this paper is the result of a shared work between Unbabel ́s linguists and Unbabel ́s AI engineering team, matured over a year. The project should, also, be taken as a statement of multidisciplinary, proving and validating the much-needed articulation between the different scientific fields that compose and characterize the area of Natural Language Processing (NLP).
%U https://aclanthology.org/2022.eamt-1.24
%P 211-219
Markdown (Informal)
[A Case Study on the Importance of Named Entities in a Machine Translation Pipeline for Customer Support Content](https://aclanthology.org/2022.eamt-1.24) (Menezes et al., EAMT 2022)
ACL