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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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| 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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| 15 |
Open Question Answering with Weakly Supervised Embedding Models ...
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Abstract:
Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/1404.4326
https://dx.doi.org/10.48550/arxiv.1404.4326
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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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