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Simple Search
Hits 1 – 7 of 7
1
Can Synthetic Translations Improve Bitext Quality? ...
Briakou, Eleftheria
;
Carpuat, Marine
. - : arXiv, 2022
BASE
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2
XFORMAL: A Benchmark for Multilingual Formality Style Transfer ...
Briakou, Eleftheria
;
Lu, Di
;
Zhang, Ke
. - : arXiv, 2021
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3
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
Briakou, Eleftheria
;
Agrawal, Sweta
;
Tetreault, Joel
. - : arXiv, 2021
BASE
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4
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Briakou, Eleftheria
;
Carpuat, Marine
. - : Underline Science Inc., 2021
BASE
Show details
5
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Joel
;
Agrawal, Sweta
. - : Underline Science Inc., 2021
BASE
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6
Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer ...
NAACL 2021 2021
;
., Joel
;
Briakou, Eleftheria
. - : Underline Science Inc., 2021
BASE
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7
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank ...
Briakou, Eleftheria
;
Carpuat, Marine
. - : arXiv, 2020
Abstract:
Detecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale. This work improves the prediction and annotation of fine-grained semantic divergences. We introduce a training strategy for multilingual BERT models by learning to rank synthetic divergent examples of varying granularity. We evaluate our models on the Rationalized English-French Semantic Divergences, a new dataset released with this work, consisting of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales. Learning to rank helps detect fine-grained sentence-level divergences more accurately than a strong sentence-level similarity model, while token-level predictions have the potential of further distinguishing between coarse and fine-grained divergences. ... : EMNLP 2020 ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://dx.doi.org/10.48550/arxiv.2010.03662
https://arxiv.org/abs/2010.03662
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