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Hits 1 – 5 of 5
1
LIBRI-LIGHT: a benchmark for asr with limited or no supervision
Kahn, Jacob
;
Rivière, Morgane
;
Zheng, Weiyi
...
In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-02959460 ; ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona / Virtual, Spain. pp.7669-7673, ⟨10.1109/ICASSP40776.2020.9052942⟩ (2020)
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2
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
Kharitonov, Eugene
;
Rivière, Morgane
;
Synnaeve, Gabriel
...
In: SLT 2020 - IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070321 ; SLT 2020 - IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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3
Unsupervised pretraining transfers well across languages
Rivière, Morgane
;
Joulin, Armand
;
Mazaré, Pierre-Emmanuel
...
In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-02959418 ; ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona / Virtual, Spain. pp.7414-7418, ⟨10.1109/ICASSP40776.2020.9054548⟩ (2020)
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4
Unsupervised pretraining transfers well across languages ...
Rivière, Morgane
;
Joulin, Armand
;
Mazaré, Pierre-Emmanuel
. - : arXiv, 2020
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5
Reference-less Quality Estimation of Text Simplification Systems
Martin, Louis
;
Humeau, Samuel
;
Mazaré, Pierre-Emmanuel
;
Bordes, Antoine
;
Villemonte de La Clergerie, Éric
;
Sagot, Benoît
In: 1st Workshop on Automatic Text Adaptation (ATA) ; https://hal.inria.fr/hal-01959054 ; 1st Workshop on Automatic Text Adaptation (ATA), Nov 2018, Tilburg, Netherlands ; https://www.ida.liu.se/~evere22/ATA-18/ (2018)
Abstract:
International audience ; The evaluation of text simplification (TS) systems remains an open challenge. As the task has common points with machine translation (MT), TS is often evaluated using MT metrics such as BLEU. However, such metrics require high quality reference data, which is rarely available for TS. TS has the advantage over MT of being a monolingual task, which allows for direct comparisons to be made between the simplified text and its original version. In this paper, we compare multiple approaches to reference-less quality estimation of sentence-level text simplification systems, based on the dataset used for the QATS 2016 shared task. We distinguish three different dimensions: gram-maticality, meaning preservation and simplicity. We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.
Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
URL:
https://hal.inria.fr/hal-01959054v2/file/main.pdf
https://hal.inria.fr/hal-01959054
https://hal.inria.fr/hal-01959054v2/document
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