Best paper award at #emnlp2018

"Phrase-Based & Neural Unsupervised Machine Translation"

Lample et al.: https://arxiv.org/abs/1804.07755

Code:
https://github.com/facebookresearch/UnsupervisedMT

Blog:
https://code.fb.com/ai-research/unsupervised-machine-translation-a-novel-approach-to-provide-fast-accurate-translations-for-more-languages/

#computation #deeplearning #language

https://t.me/ArtificialIntelligenceArticles

Neural Approaches to Conversational AI

Gao et al.: https://arxiv.org/abs/1809.08267

#computation #language #machinelearning

Language GANs Falling Short"

Caccia et al.: https://arxiv.org/pdf/1811.02549.pdf

#artificialintelligence #deeplearning #generativeadversarialnetworks #language #machinelearning

Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
By Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl, Caiming Xiong, Richard Socher, Pascale Fung: https://arxiv.org/abs/1905.08743
#Computation #Language #ArtificialIntelligence

"Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts"
Bullock et al.: https://arxiv.org/abs/1906.01946
#Computation #Language #AIEthics #AIGovernance #ArtificialIntelligence

@ArtificialIntelligenceArticles

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

Jiang et al.: https://arxiv.org/abs/1906.07343

#reinforcementlearning #language #machinelearning

Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
Luo et al.: https://arxiv.org/abs/1906.06718
#ArtificialIntelligence #Computation #Language

Explain Yourself! Leveraging Language Models for Commonsense Reasoning
Rajani et al.: https://arxiv.org/abs/1906.02361
Github: https://github.com/salesforce/cos-e
Blog: https://blog.einstein.ai/leveraging-language-models-for-commonsense/
#Computation #Language #MachineLearning

"Two neural nets learn to communicate through their own emergent visual language"
Here set in clay tablets.
By Joel Simon : https://github.com/joel-simon/dimensions-of-dialogue/blob/master/emergent_characters.ipynb
#language #neuralnetwork #deeplearning

ParaQG: A System for Generating Questions and Answers from Paragraphs
Kumar et al.: https://arxiv.org/abs/1909.01642
#ArtificialIntelligence #Language #MachineLearning

Deep networks work by learning complex, often hierarchical internal representations of input data. These form a kind of functional language the network uses to describe the data.

Language can emerge from tasks like object recognition: has pointy ears, whiskers, tail => cat.

This relates to Wittgenstein’s "language-game" in Philosophical Investigations, where a functional language emerge from simple tasks before defining a vocabulary.

The visual vocabulary of a convolutional neural network seems to emerge from low level features such as edges and orientations, and builds up textures, patterns and composites, … and builds up even further into complete objects: houses, dogs, etc.

Source: NeurIPS 2018“Unsupervised Deep Learning” Tutorial – Part 1 by Alex Graves - https://media.neurips.cc/Conferences/NIPS2018/Slides/Deep_Unsupervised_Learning.pdf

#artificialintelligence #deeplearning #language #machinelearning

FlauBERT: Unsupervised Language Model Pre-training for French
Le et al.: https://arxiv.org/abs/1912.05372
#Computation #Language #MachineLearning