STACL: Simultaneous Translation with Integrated Anticipation and Controllable Latency
Baidu technology presented at #NIPS2018
Website: https://simultrans-demo.github.io
ArXiV: https://arxiv.org/abs/1810.08398
#NLP #translation #Baidu
Baidu technology presented at #NIPS2018
Website: https://simultrans-demo.github.io
ArXiV: https://arxiv.org/abs/1810.08398
#NLP #translation #Baidu
Memory Transformer
Burtsev & Sapunov
The authors proposed and studied two memory augmented architectures MemTransformer and MemBottleneck Transformer. Qualitative analysis of attention patterns produced by the transformer heads trained to solve machine translation task suggests that both models successfully discovered basic operations for memory control. Attention maps show evidence for the presence of memory read/write as well as some in-memory processing operations such as copying and summation.
A comparison of machine translation quality shows that adding general-purpose memory in MemTransformer improves performance over the baseline. Moreover, the speed of training and final quality positively correlates with the memory size. On the other hand, MemBottleneck Transformer, with all self-attention restricted to the memory only, has significantly lower scores after training.
Memory lesion tests demonstrate that the performance of the pre-trained MemTransformer model critically depends on the presence of memory. Still, the memory controller learned by the model degrades only gradually when memory size is changed during inference. This indicates that the controller has some robustness and ability for generalization.
More interesting figures u can check out in the attachment.
paper: https://arxiv.org/abs/2006.11527.pdf
#nlp #transformer #attention #machine #translation
Burtsev & Sapunov
The authors proposed and studied two memory augmented architectures MemTransformer and MemBottleneck Transformer. Qualitative analysis of attention patterns produced by the transformer heads trained to solve machine translation task suggests that both models successfully discovered basic operations for memory control. Attention maps show evidence for the presence of memory read/write as well as some in-memory processing operations such as copying and summation.
A comparison of machine translation quality shows that adding general-purpose memory in MemTransformer improves performance over the baseline. Moreover, the speed of training and final quality positively correlates with the memory size. On the other hand, MemBottleneck Transformer, with all self-attention restricted to the memory only, has significantly lower scores after training.
Memory lesion tests demonstrate that the performance of the pre-trained MemTransformer model critically depends on the presence of memory. Still, the memory controller learned by the model degrades only gradually when memory size is changed during inference. This indicates that the controller has some robustness and ability for generalization.
More interesting figures u can check out in the attachment.
paper: https://arxiv.org/abs/2006.11527.pdf
#nlp #transformer #attention #machine #translation
Most of the Scots NLP models used Wikipedia for training are wrong
One person who had done 200,000 edits and written 20,000 articles of Scots Wikipedia was not using Scots language but rather faking it. Since Wikipedia texts are often used as a dataset for #NLU / #NLP / #NMT neural nets training, those models using it as an input had a flaw.
Reddit thread: https://www.reddit.com/r/Scotland/comments/ig9jia/ive_discovered_that_almost_every_single_article/
#datasets #translation #scots #wikipedia
One person who had done 200,000 edits and written 20,000 articles of Scots Wikipedia was not using Scots language but rather faking it. Since Wikipedia texts are often used as a dataset for #NLU / #NLP / #NMT neural nets training, those models using it as an input had a flaw.
Reddit thread: https://www.reddit.com/r/Scotland/comments/ig9jia/ive_discovered_that_almost_every_single_article/
#datasets #translation #scots #wikipedia
Reddit
From the Scotland community on Reddit: I’ve discovered that almost every single article on the Scots version of Wikipedia is written…
Explore this post and more from the Scotland community
🦜 Hi!
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We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
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- ODS Slack through invite form at website
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ultimate_posts/where_to_start at master · open-data-science/ultimate_posts
Ultimate posts for opendatascience telegram channel - open-data-science/ultimate_posts