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1
Neural-Driven Search-Based Paraphrase Generation
In: 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021) ; https://hal.archives-ouvertes.fr/hal-03540926 ; 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021), Apr 2021, Kiev, Ukraine (2021)
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2
Corpus design for expressive speech: impact of the utterance length
In: 10th International Conference on Speech Prosody 2020 ; https://hal.archives-ouvertes.fr/hal-02874005 ; 10th International Conference on Speech Prosody 2020, May 2020, Tokyo, Japan. pp.955-959, ⟨10.21437/SpeechProsody.2020-195⟩ (2020)
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3
Mama/Papa, Is this Text for Me?
In: International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03112352 ; International Conference on Computational Linguistics, Dec 2020, Virtuel, Spain (2020)
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4
Towards the Automatic Processing of Language Registers: Semi-supervisedly Built Corpus and Classifier for French
In: International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) ; https://hal.archives-ouvertes.fr/hal-02064694 ; International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), Apr 2019, La Rochelle, France (2019)
Abstract: International audience ; Language registers are a strongly perceptible characteristic of texts and speeches. However, they are still poorly studied in natural language processing. In this paper, we present a semi-supervised approach which jointly builds a corpus of texts labeled in registers and an associated classifier. This approach relies on a small initial seed of expert data. After massively retrieving web pages, it iteratively alternates the training of an intermediate classifier and the annotation of new texts to augment the labeled corpus. The approach is applied to the casual, neutral, and formal registers, leading to a 750M word corpus and a final neural classifier with an acceptable performance.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
URL: https://hal.archives-ouvertes.fr/hal-02064694/document
https://hal.archives-ouvertes.fr/hal-02064694
https://hal.archives-ouvertes.fr/hal-02064694/file/paper_58.pdf
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5
The IRISA Text-To-Speech System for the Blizzard Challenge 2017
In: Blizzard Challenge ; https://hal.inria.fr/hal-01662361 ; Blizzard Challenge, Aug 2017, Stockholm, Sweden (2017)
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6
The IRISA Text-To-Speech System for the Blizzard Challenge 2016
In: Blizzard Challenge 2016 workshop ; https://hal.inria.fr/hal-01375897 ; Blizzard Challenge 2016 workshop, Sep 2016, Cupertino, United States (2016)
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7
Do not build your TTS training corpus randomly
In: Proceedings of the European Signal Processing Conference (EUSIPCO) ; https://hal.inria.fr/hal-01199083 ; Proceedings of the European Signal Processing Conference (EUSIPCO), Aug 2015, Nice, France (2015)
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8
Large Linguistic Corpus Reduction with SCP algorithms
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.inria.fr/hal-01135089 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2015, 41 (3), pp.30. ⟨10.1162/COLIa00225⟩ ; http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00225 (2015)
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9
The IRISA Text-To-Speech System for the Blizzard Challenge 2015
In: Blizzard Challenge 2015 Workshop ; https://hal.inria.fr/hal-01196168 ; Blizzard Challenge 2015 Workshop, Sep 2015, Berlin, Germany. 4 p., 2 columns (2015)
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10
Lexicons or phrase tables? An investigation in sampling-based multilingual alignment
In: 3rd Workshop on Example-­Based Machine Translation ; https://hal.archives-ouvertes.fr/hal-00439806 ; 3rd Workshop on Example-­Based Machine Translation, Nov 2009, Dublin, Ireland. pp.45-52 (2009)
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