Semanlink - Neural machine translation

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Wikipedia Neural machine translation

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Masakhane

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27 Documents (Liste longue)

Text Translation cognitive skill - Azure Cognitive Search | Microsoft Learn

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2023-05-09 A propos

Inria Paris NLP (ALMAnaCH team) sur Twitter : “Writing in two languages: Neural machine translation as an assistive bilingual writing tool”

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2023-03-13 A propos

[2302.04907] Binarized Neural Machine Translation

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2023-02-13 A propos

joeynmt/joeynmt: Minimalist NMT for educational purposes

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Neural machine translation

2023-01-05 A propos

Colin Leong sur Twitter : "This book is about the only "dataset" I ever found for Hani. My first ever foray into the field, I found an electronic copy and munged it into a Hani/English parallel corpus, and trained a JoeyNMT model with the help of @MasakhaneNLP and @KreutzerJulia in particular." / Twitter

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2023-01-05 A propos

Meta AI sur Twitter : "Universal Speech Translator (UST) is the 1st AI-powered speech-to-speech translation system for a primarily oral language..."

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2022-10-19 A propos

UKPLab/EasyNMT: Easy to use, state-of-the-art Neural Machine Translation for 100+ languages

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2022-10-14 A propos

How to Train an mT5 Model for Translation With Simple Transformers | by Thilina Rajapakse | Towards Data Science

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2022-09-25 A propos

Continuous Learning in NMT using Bilingual Dictionaries

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2022-09-17 A propos

VaLaR NMT: Vastly Lacking Resources Neural Machine Translation (2019)

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2021-10-14 A propos

NMT Training through the Lens of SMT

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2021-09-07 A propos

Joey NMT’s documentation!

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Neural machine translation

2021-08-26 A propos

[1906.05685] A Focus on Neural Machine Translation for African Languages

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2021-06-30 A propos

Nils Reimers sur Twitter : EasyNMT Easy-to-use (3 lines of code), state-of-the-art neural machine translations

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2021-04-27 A propos

GitHub - OpenNMT/OpenNMT-py: Open Source Neural Machine Translation in PyTorch

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2020-01-17 A propos

Jade Abbott sur Twitter : "Calling all African NLPers! Goal: Let's publish a paper with NMT baselines for all African languages Slides

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2019-09-02 A propos

[1812.04616] Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs

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2018-12-14 A propos

Monolingual data in NMT

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2018-12-04 A propos

Paris NLP Season 3 Meetup #2 – Paris NLP

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2018-12-04 A propos

Using Monolingual Data in Neural Machine Translation: a Systematic Study

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2018-11-28 A propos

Encoder-Decoder Recurrent Neural Network Models for Neural Machine Translation

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2018-11-07 A propos

[1810.04805] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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2018-10-12 A propos

Found in translation: Building a language translator from scratch with deep learning - FloydHub

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Neural machine translation

2018-10-11 A propos

The Illustrated Transformer – Jay Alammar

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2018-07-09 A propos

TensorFlow Neural Machine Translation (seq2seq) Tutorial

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2017-09-18 A propos

Research Blog: Transformer: A Novel Neural Network Architecture for Language Understanding

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2017-09-01 A propos

Attention and Memory in Deep Learning and NLP – WildML

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cf. visual attention

In standard [#seq2seq](/tag/sequence_to_sequence_learning) NMT, the decoder is supposed to generate a translation solely based on the last hidden state of the encoder - which therefore must capture everything from the source sentence (it must be a sentence embedding). Not good. Hence the attention mechanism.

> we allow the decoder to “attend” to different parts of the source sentence at each step of the output generation. Importantly, we let the model learn what to attend to based on the input sentence and what it has produced so far

> each decoder output word now depends on a weighted combination of all the input states, not just the last state.

Possible to interpret what the model is doing by looking at the Attention weight matrix

Cost: We need to calculate an attention value for each combination of input and output word (-> attention is a bit of a misnomer: we look at everything in details before deciding what to focus on)

> attention mechanism is simply giving the network access to its internal memory, which is the hidden state of the encoder

> Unlike typical memory, the memory access mechanism here is soft, which means that the network retrieves a weighted combination of all memory locations, not a value from a single discrete location

2016-01-03 A propos

Propriétés

sl:creationDate : 2016-01-03
sl:creationTime : 2016-01-03T14:38:26Z
sl:describedBy : https://en.wikipedia.org/wiki/Neural_machine_translation
rdf:type : sl:Tag
skos:altLabel : NMT@en
skos:prefLabel : Neural machine translation@fr