2 |
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Machine Translation for the Normalisation of 17th c. French ; Traduction automatique pour la normalisation du français du XVII e siècle
|
|
|
|
In: TALN 2020 ; https://hal.archives-ouvertes.fr/hal-02596669 ; TALN 2020, ATALA, Jun 2020, Nancy, France (2020)
|
|
BASE
|
|
Show details
|
|
6 |
Findings of the 2019 Conference on Machine Translation (WMT19)
|
|
|
|
In: Barrault, Loïc orcid:0000-0002-0634-6147 , Bojar, Ondřej orcid:0000-0002-0606-0050 , Costa-Jussà, Marta R. orcid:0000-0002-5703-520X , Federmann, Christian, Fishel, Mark and Graham, Yvette (2019) Findings of the 2019 Conference on Machine Translation (WMT19). In: Fourth Conference on Machine Translation, 1-2 Aug 2019, Florence, Italy. (2019)
|
|
BASE
|
|
Show details
|
|
7 |
A Workflow For On The Fly Normalisation Of 17th c. French
|
|
|
|
In: DH2019 ; https://hal.archives-ouvertes.fr/hal-02276150 ; DH2019, ADHO, Jul 2019, Utrecht, Netherlands (2019)
|
|
BASE
|
|
Show details
|
|
8 |
What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
|
|
|
|
In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01898412 ; ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.2126-2136 (2018)
|
|
BASE
|
|
Show details
|
|
9 |
What you can cram into a single vector: Probing sentence embeddings for linguistic properties ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Word Representations in Factored Neural Machine Translation
|
|
|
|
In: Proceedings of the Conference on Machine Translation (WMT), ; Conference on Machine Translation ; https://hal.archives-ouvertes.fr/hal-01618384 ; Conference on Machine Translation, Association for Computational Linguistics, Sep 2017, Copenhagen, Denmark. pp.43 - 55 (2017)
|
|
BASE
|
|
Show details
|
|
11 |
Neural Machine Translation by Generating Multiple Linguistic Factors
|
|
|
|
In: 5th International Conference Statistical Language and Speech Processing SLSP 2017 ; https://hal-univ-lemans.archives-ouvertes.fr/hal-01689270 ; 5th International Conference Statistical Language and Speech Processing SLSP 2017, Oct 2017, Le Mans, France. ⟨10.1007/978-3-319-68456-7_2⟩ (2017)
|
|
BASE
|
|
Show details
|
|
12 |
NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems
|
|
|
|
In: ISSN: 1804-0462 ; The Prague Bulletin of Mathematical Linguistics ; https://hal-univ-lemans.archives-ouvertes.fr/hal-01647873 ; The Prague Bulletin of Mathematical Linguistics, Univerzita Karlova v Praze, 2017, 109 (1), ⟨10.1515/pralin-2017-0035⟩ (2017)
|
|
BASE
|
|
Show details
|
|
13 |
Very Deep Convolutional Networks for Text Classification
|
|
|
|
In: European Chapter of the Association for Computational Linguistics EACL'17 ; https://hal.archives-ouvertes.fr/hal-01454940 ; European Chapter of the Association for Computational Linguistics EACL'17, 2017, Valencia, Spain (2017)
|
|
BASE
|
|
Show details
|
|
14 |
Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Neural Machine Translation by Generating Multiple Linguistic Factors ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems
|
|
|
|
In: Prague Bulletin of Mathematical Linguistics , Vol 109, Iss 1, Pp 15-28 (2017) (2017)
|
|
BASE
|
|
Show details
|
|
17 |
Multimodal Attention for Neural Machine Translation ...
|
|
|
|
Abstract:
The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simultaneously focus over an image and its natural language description for generating a description in another language. We train several variants of our proposed attention mechanism on the Multi30k multilingual image captioning dataset. We show that a dedicated attention for each modality achieves up to 1.6 points in BLEU and METEOR compared to a textual NMT baseline. ... : 10 pages, under review COLING 2016 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Neural and Evolutionary Computing cs.NE
|
|
URL: https://dx.doi.org/10.48550/arxiv.1609.03976 https://arxiv.org/abs/1609.03976
|
|
BASE
|
|
Hide details
|
|
19 |
OCR Error Correction Using Statistical Machine Translation
|
|
|
|
In: 16th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2015). ; https://hal.archives-ouvertes.fr/hal-01433200 ; 16th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2015)., 2015, Cairo, Egypt (2015)
|
|
BASE
|
|
Show details
|
|
20 |
Continuous Adaptation to User Feedback for Statistical Machine Translation
|
|
|
|
In: North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015) ; https://hal.archives-ouvertes.fr/hal-01454944 ; North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015), 2015, Denver (Colorado, USA), Unknown Region (2015)
|
|
BASE
|
|
Show details
|
|
|
|