@inproceedings{stojanovski-etal-2020-contracat,
title = "{C}ontra{CAT}: Contrastive Coreference Analytical Templates for Machine Translation",
author = "Stojanovski, Dario and
Krojer, Benno and
Peskov, Denis and
Fraser, Alexander",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.417",
doi = "10.18653/v1/2020.coling-main.417",
pages = "4732--4749",
abstract = "Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well. ContraPro is a notable example of a contrastive challenge set for English→German pronoun translation. The high scores achieved by transformer models may suggest that they are able to effectively model the complicated set of inferences required to carry out pronoun translation. This entails the ability to determine which entities could be referred to, identify which entity a source-language pronoun refers to (if any), and access the target-language grammatical gender for that entity. We first show through a series of targeted adversarial attacks that in fact current approaches are not able to model all of this information well. Inserting small amounts of distracting information is enough to strongly reduce scores, which should not be the case. We then create a new template test set ContraCAT, designed to individually assess the ability to handle the specific steps necessary for successful pronoun translation. Our analyses show that current approaches to context-aware NMT rely on a set of surface heuristics, which break down when translations require real reasoning. We also propose an approach for augmenting the training data, with some improvements.",
}
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<abstract>Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well. ContraPro is a notable example of a contrastive challenge set for English→German pronoun translation. The high scores achieved by transformer models may suggest that they are able to effectively model the complicated set of inferences required to carry out pronoun translation. This entails the ability to determine which entities could be referred to, identify which entity a source-language pronoun refers to (if any), and access the target-language grammatical gender for that entity. We first show through a series of targeted adversarial attacks that in fact current approaches are not able to model all of this information well. Inserting small amounts of distracting information is enough to strongly reduce scores, which should not be the case. We then create a new template test set ContraCAT, designed to individually assess the ability to handle the specific steps necessary for successful pronoun translation. Our analyses show that current approaches to context-aware NMT rely on a set of surface heuristics, which break down when translations require real reasoning. We also propose an approach for augmenting the training data, with some improvements.</abstract>
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%0 Conference Proceedings
%T ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation
%A Stojanovski, Dario
%A Krojer, Benno
%A Peskov, Denis
%A Fraser, Alexander
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F stojanovski-etal-2020-contracat
%X Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well. ContraPro is a notable example of a contrastive challenge set for English→German pronoun translation. The high scores achieved by transformer models may suggest that they are able to effectively model the complicated set of inferences required to carry out pronoun translation. This entails the ability to determine which entities could be referred to, identify which entity a source-language pronoun refers to (if any), and access the target-language grammatical gender for that entity. We first show through a series of targeted adversarial attacks that in fact current approaches are not able to model all of this information well. Inserting small amounts of distracting information is enough to strongly reduce scores, which should not be the case. We then create a new template test set ContraCAT, designed to individually assess the ability to handle the specific steps necessary for successful pronoun translation. Our analyses show that current approaches to context-aware NMT rely on a set of surface heuristics, which break down when translations require real reasoning. We also propose an approach for augmenting the training data, with some improvements.
%R 10.18653/v1/2020.coling-main.417
%U https://aclanthology.org/2020.coling-main.417
%U https://doi.org/10.18653/v1/2020.coling-main.417
%P 4732-4749
Markdown (Informal)
[ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation](https://aclanthology.org/2020.coling-main.417) (Stojanovski et al., COLING 2020)
ACL