Language independent nlp with deep learning

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Language Independent NLP with Deep Learning, VANDA, www.vanda-project.de

Data & Analytics

Language-Independent NLP with
Deep Learning
Raphael Villedieu, Roman Teucher
19/20.06.2019
www.deecoob.com

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1. Use Case at deecoob
2. Current mono-lingual approaches
3. International Use Case
4. Transformer Models
5. Multi-language capabilities
Overview

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1. Use Case at deecoob

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● pre-labeled text corpus
● Tf-Idf as text features
● Naïve Bayes as classiﬁer
2. Current mono-lingual approaches

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● Music event detection world-wide
○ different languages
○ texts containing more than one language
○ different character sets (umlauts, accents,
cyrillic, hebrew)
3. International Use Case

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3. International Use Case : Naïve Approach

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3. International Use Case : BERT approach

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● use deep neural network
encoder-decoder
○ can process multiple languages at once
○ learns language-independent model
● input entire sequence at once
● make heavy use of attention
4. Transformer Models

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Encoder-Decoder Stack
4. Transformer - Google BERT
http://jalammar.github.io/illustrated-transformer/

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4. Transformer - Google BERT
https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/
model token dependencies through
multi-head self-attention

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Step 1 :
● training on masked words
● randomly mask 15% of words
○ words “do not see themselves” in training
● Trained with Wikipedia corpus 104 languages, 12-layer, 768-hidden, 12-heads, 110M
parameters
4. Transformer - Google BERT
https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html

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Step 2 : two-sentence training
4. Transformer - Google BERT
https://www.kdnuggets.com/2018/12/bert-sota-nlp-model-explained.html

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Data - Information - InsightData - Information - Insight
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5. Multi-language capabilities

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Data - Information - InsightData - Information - Insight
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Results

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Data - Information - Insight
deecoob Technology GmbH
+49 (0) 351 410 470
www.deecoob.com
info@deecoob.com

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怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

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怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

Case Study 4 Where the cry of rebellion happen?

Language independent nlp with deep learning

1. Language-Independent NLP with Deep Learning Raphael Villedieu, Roman Teucher 19/20.06.2019 www.deecoob.com

2. www.deecoob.com 1 Data - Information - InsightData - Information - Insight www.deecoob.com 1 1. Use Case at deecoob 2. Current mono-lingual approaches 3. International Use Case 4. Transformer Models 5. Multi-language capabilities Overview

3. www.deecoob.com 2 Data - Information - InsightData - Information - Insight www.deecoob.com 2 1. Use Case at deecoob

4. www.deecoob.com 3 Data - Information - InsightData - Information - Insight www.deecoob.com 3 ● pre-labeled text corpus ● Tf-Idf as text features ● Naïve Bayes as classiﬁer 2. Current mono-lingual approaches

5. www.deecoob.com 4 Data - Information - InsightData - Information - Insight www.deecoob.com 4 ● Music event detection world-wide ○ different languages ○ texts containing more than one language ○ different character sets (umlauts, accents, cyrillic, hebrew) 3. International Use Case

6. www.deecoob.com 5 Data - Information - InsightData - Information - Insight www.deecoob.com 5 3. International Use Case : Naïve Approach

7. www.deecoob.com 6 Data - Information - InsightData - Information - Insight www.deecoob.com 6 3. International Use Case : BERT approach

8. www.deecoob.com 7 Data - Information - InsightData - Information - Insight www.deecoob.com 7 ● use deep neural network encoder-decoder ○ can process multiple languages at once ○ learns language-independent model ● input entire sequence at once ● make heavy use of attention 4. Transformer Models

9. www.deecoob.com 8 Data - Information - InsightData - Information - Insight www.deecoob.com 8 Encoder-Decoder Stack 4. Transformer - Google BERT http://jalammar.github.io/illustrated-transformer/

10. www.deecoob.com 9 Data - Information - InsightData - Information - Insight www.deecoob.com 9 4. Transformer - Google BERT https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/ model token dependencies through multi-head self-attention

11. www.deecoob.com 10 Data - Information - InsightData - Information - Insight www.deecoob.com 10 Step 1 : ● training on masked words ● randomly mask 15% of words ○ words “do not see themselves” in training ● Trained with Wikipedia corpus 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters 4. Transformer - Google BERT https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html

12. www.deecoob.com 11 Data - Information - InsightData - Information - Insight www.deecoob.com 11 Step 2 : two-sentence training 4. Transformer - Google BERT https://www.kdnuggets.com/2018/12/bert-sota-nlp-model-explained.html

13. www.deecoob.com 12 Data - Information - InsightData - Information - Insight www.deecoob.com 12 5. Multi-language capabilities

14. www.deecoob.com 13 Data - Information - InsightData - Information - Insight www.deecoob.com 13 Results

15. www.deecoob.com 14 Data - Information - Insight deecoob Technology GmbH +49 (0) 351 410 470 www.deecoob.com info@deecoob.com

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