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Multilingual Unsupervised Sentence Simplification
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In: https://hal.inria.fr/hal-03109299 ; 2021 (2021)
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Controllable Sentence Simplification
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02678214 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org/proceedings/lrec2020/index.html (2020)
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
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In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889823 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States (2020)
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Augmenting Transformers with KNN-Based Composite Memory for Dialog
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02999678 ; Transactions of the Association for Computational Linguistics, The MIT Press, In press, ⟨10.1162/tacl_a_00356⟩ ; https://transacl.org/index.php/tacl (2020)
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MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ...
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ASSET: A dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations
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Controllable Sentence Simplification
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In: https://hal.inria.fr/hal-02445874 ; 2019 (2019)
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Reference-less Quality Estimation of Text Simplification Systems
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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)
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| 11 |
Fader Networks: Manipulating Images by Sliding Attributes
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In: 31st Conference on Neural Information Processing Systems (NIPS 2017) ; https://hal.archives-ouvertes.fr/hal-02275215 ; 31st Conference on Neural Information Processing Systems (NIPS 2017), Dec 2017, Long Beach, CA, United States. pp.5969-5978 (2017)
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Abstract:
International audience ; This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images.
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Keyword:
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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URL: https://hal.archives-ouvertes.fr/hal-02275215/file/1706.00409.pdf
https://hal.archives-ouvertes.fr/hal-02275215
https://hal.archives-ouvertes.fr/hal-02275215/document
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| 12 |
Extracting biomedical events from pairs of text entities
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.archives-ouvertes.fr/hal-01313324 ; BMC Bioinformatics, BioMed Central, 2015, 16 (Suppl 10), pp.S8. ⟨10.1186/1471-2105-16-S10-S8⟩ ; http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-16-S10-S8 (2015)
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Open Question Answering with Weakly Supervised Embedding Models
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In: European Conference (ECML PKDD 2014) ; https://hal.archives-ouvertes.fr/hal-01344007 ; European Conference (ECML PKDD 2014), Sep 2014, nancy, France. pp.165-180, ⟨10.1007/978-3-662-44848-9_11⟩ (2014)
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Fast recursive multi-class classification of pairs of text entities for biomedical event extraction
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In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01060830 ; Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2014, Gothenburg, Sweden. pp.692--701 (2014)
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Open Question Answering with Weakly Supervised Embedding Models ...
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Towards Understanding Situated Natural Language
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In: 13th International Conference on Artificial Intelligence and Statistics ; https://hal.archives-ouvertes.fr/hal-00750937 ; 13th International Conference on Artificial Intelligence and Statistics, May 2010, Chia Laguna Resort, Sardinia, Italy. pp.65-72 (2010)
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Extracting biomedical events from pairs of text entities
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.archives-ouvertes.fr/hal-01278279 ; BMC Bioinformatics, BioMed Central, 2005, 16 (Suppl 10), pp.S8. ⟨10.1186/1471-2105-16-S10-S8⟩ ; http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-16-S10-S8 (2005)
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