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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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Text Generation with and without Retrieval ; Génération de textes basés sur la connaissance avec et sans recherche
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In: https://hal.univ-lorraine.fr/tel-03542634 ; Computer Science [cs]. Université de Lorraine, 2021. English. ⟨NNT : 2021LORR0164⟩ (2021)
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The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation ...
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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
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Alternative Input Signals Ease Transfer in Multilingual Machine Translation ...
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AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ...
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Multilingual AMR-to-Text Generation
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In: 2020 Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-02999676 ; 2020 Conference on Empirical Methods in Natural Language Processing, Nov 2020, Punta Cana, Dominican Republic (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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Abstract:
International audience ; Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augmenting generative Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialog modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge required for knowledgeable but engaging dialog from Wikipedia, images, and human-written dialog utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.archives-ouvertes.fr/hal-02999678/file/2419-Fan-finalversion.pdf https://hal.archives-ouvertes.fr/hal-02999678 https://hal.archives-ouvertes.fr/hal-02999678/document https://doi.org/10.1162/tacl_a_00356
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Multilingual Translation with Extensible Multilingual Pretraining and Finetuning ...
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Beyond English-Centric Multilingual Machine Translation ...
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MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
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