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Chatbotsummit - Chatbots for the Enterprise will not tell the weather
- 6. Domain Knowledge Existing Datasets Pretrained Models Single-usecase Tuning Common Language No Privacy Blockers Crowd Research Industry-specific Single Language Multi-layer corpora Enterprise entities/concepts Cross-domain representation Performance guarantees (accuracy, ambiguity) WWW/OS How to generalize cross-use case, harmonize cross-domain How to enable small and large domains
- 7. Computational Linguistics “changing insurance” “I see that my check shows me 76 hours, command style multiple facts references Segmentation POS Stemming Disambiguation “apple” “show room” “How to change name?” context meaning VERB fruit|company|customer normalization change but I actually worked 80. How can we resolve this?”
- 8. Computational Linguistics “changing insurance” “I see that my check shows me 76 hours, command style multiple facts references Segmentation POS Stemming Disambiguation “apple” “show room” “How to change name?” context meaning Dependency Parsing Patterns ”Show me 10 invoices from Acme” VERB PRON NUM NOUN ADP PROPN num mod obj obj num mod case leverage dependency representation complex nesting maintain rules/patterns, ‘regex’ hell coverage VERB fruit|company|customer normalization rules change but I actually worked 80. How can we resolve this?” show I 10 invoice from Acme
- 9. Computational Linguistics “changing insurance” “I see that my check shows me 76 hours, command style multiple facts references Segmentation POS Stemming Disambiguation “apple” “show room” “How to change name?” context meaning Dependency Parsing Patterns ”Show me 10 invoices from Acme” VERB PRON NUM NOUN ADP PROPN num mod obj obj num mod case Entity Extraction “All partly-paid POs in New York for Acme from March” status attribute purchase orders US city customer | vendor date (month) leverage dependency representation complex nesting maintain rules/patterns, ‘regex’ hell coverage spelling errors synonyms standard & custom entities VERB fruit|company|customer normalization rules facts change but I actually worked 80. How can we resolve this?” show I 10 invoice from Acme
- 10. Computational Linguistics “changing insurance” “I see that my check shows me 76 hours, command style multiple facts references Segmentation POS Stemming Disambiguation “apple” “show room” “How to change name?” context meaning Dependency Parsing Patterns ”Show me 10 invoices from Acme” VERB PRON NUM NOUN ADP PROPN num mod obj obj num mod case Entity Extraction “All partly-paid POs in New York for Acme from March” status attribute purchase orders US city customer | vendor date (month) leverage dependency representation complex nesting maintain rules/patterns, ‘regex’ hell coverage spelling errors synonyms standard & custom entities VERB fruit|company|customer normalization rules facts change but I actually worked 80. How can we resolve this?” show I 10 invoice from Acme How to scale for 1000s dialogs, 10s languages, 1000s customers How to scale semantic robustness, spelling error tolerance
- 11. Computational Linguistics “changing insurance” “I see that my check shows me 76 hours, command style multiple facts references Segmentation POS Stemming Disambiguation “apple” “show room” “How to change name?” context meaning Dependency Parsing Patterns ”Show me 10 invoices from Acme” VERB PRON NUM NOUN ADP PROPN num mod obj obj num mod case Entity Extraction “All partly-paid POs in New York for Acme from March” status attribute purchase orders US city customer | vendor date (month) leverage dependency representation complex nesting maintain rules/patterns, ‘regex’ hell coverage spelling errors synonyms standard & custom entities VERB fruit|company|customer normalization rules facts change but I actually worked 80. How can we resolve this?” show I 10 invoice from Acme How to scale for 1000s dialogs, 10s languages, 1000s customers How to scale semantic robustness, spelling error tolerance “Natural Language Conversation is one of the most challenging artificial intelligence problems …” Lifeng Shang (Huawei)
- 18. Machine|Deep Learning for NLP on Steroids Word vectors Characters Ngrams BOW The right input representation Zero training data Crowdsourcing Cross-language Dataset Word2Vec, GloVe, … TF/IDF WMD CNN RNN LSTM Algorithm BLSTM-CNN SVM-TF/IDF Any comb. Cool algorithms POS Entities SRL Generate training data Additional features Pipeline Memory, CPU/GPU Precision/Recall, F1 Hyper parameters Example size Train models Choose algorithm/model Multi-language Black box Maintainability Automation
- 19. Machine|Deep Learning for NLP on Steroids Word vectors Characters Ngrams BOW The right input representation Zero training data Crowdsourcing Cross-language Dataset Word2Vec, GloVe, … TF/IDF WMD CNN RNN LSTM Algorithm BLSTM-CNN SVM-TF/IDF Any comb. Cool algorithms POS Entities SRL Generate training data Additional features Pipeline Memory, CPU/GPU Precision/Recall, F1 Hyper parameters Example size Train models Choose algorithm/model Multi-language Black box Maintainability Automation ML complexity input, algorithm ensemble, performance How to automate, model selection, tuning, user validation
- 27. Takeaway Thank You!• • • • • • karsten.schmidt01@sap.com ML/DL for Chatbots Hard to Generalize Depends on Use Case and Data De Tuning vs. Algorithm Selection Automation and Blueprints Combination of Linguistic Analysis and Deep Learning Specializing for Domains
- 28. Takeaway ML/DL for Chatbots Hard to Generalize Depends on Use Case and Data De Tuning vs. Algorithm Selection Automation and Blueprints Combination of Linguistic Analysis and Deep Learning Specializing for Domains karsten.schmidt01@sap.com