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Hits 1 – 5 of 5
1
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Esplà-Gomis, Miquel
;
Pérez-Ortiz, Juan Antonio
. - : Underline Science Inc., 2021
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2
Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation ...
The 28th International Conference on Computational Linguistics 2020
;
Pérez-Ortiz, Juan Antonio
;
Sánchez-Cartagena, Víctor Manuel
. - : Underline Science Inc., 2020
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3
Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
Sanchez-Cartagena, Victor Manuel
;
Toral, Antonio
;
Klubicka, Filip
In: Articles (2018)
Abstract:
This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to- Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of Slavic languages, which made the annotation process feasible and accurate. Errors in MT outputs were then annotated by two annotators following this taxonomy. Subsequently, we carried out a statistical analysis which showed that the best-performing system (neural) reduces the errors produced by the worst system (pure phrase-based) by more than half (54%). Moreover, we conducted an additional analysis of agreement errors in which we distinguished between short (phrase-level) and long distance (sentence-level) errors. We discovered that phrase-based MT approaches are of limited use for long distance agreement phenomena, for which neural MT was found to be especially effective.
Keyword:
Computational Engineering
;
Digital Humanities
;
error annotation
;
factored models
;
human evaluation
;
Language Interpretation and Translation
;
Modern Languages
;
multidimensional quality metrics (MQM)
;
neural machine translation
;
Other Computer Engineering
;
phrase-based machine translation
;
Slavic Languages and Societies
;
statistical machine translation
URL:
https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1062&context=scschcomart
https://arrow.tudublin.ie/scschcomart/57
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4
ScaleMT: a free/open-source framework for building scalable machine translation web services
Sánchez-Cartagena, Víctor Manuel
;
Pérez-Ortiz, Juan Antonio
In:
The Prague bulletin of mathematical linguistics. - Praha : Univ.
(2010) 93, 97-106
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OLC Linguistik
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5
Tradubi: open-source social translation for the Apertium machine translation platform
Pérez-Ortiz, Juan Antonio
;
Sánchez-Cartagena, Víctor Manuel
In:
The Prague bulletin of mathematical linguistics. - Praha : Univ.
(2010) 93, 47-56
BLLDB
OLC Linguistik
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