enfore Chatbot HamburgAI
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Chatbots - "Recent improvements in the area of Natural Language Processing gave rise to the new generation of Chatbots. We will look at how we have approached the problem of building a Chatbot for our customers here at enfore using rule-based systems, Word Embeddings and Deep Learning. Which approaches worked, which ones didn't and why." Nils Grabbert, VP Data Science & Miroslav Zoričák, Machine Learning Engineer, enfore
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enfore Chatbot HamburgAI
- 1. enfore Chatbot Nils Grabbert, Miroslav Zoričák
- 2. Run your business anywhere, anytime and on any device: in your store, at the office, in your warehouse, on-site with a customer or supplier and on the go - using a smart phone, tablet, computer or enfore Business Device. enfore is building an open business platform to develop solutions that help 200 million small businesses around the world to run their business
- 3. data science @ enfore • 3 years ago we started to build everything from scratch • building the infrastructure • data/ml pipelines • prediction services • taking a DevOps approach in everything we do • now a team of 9 employees • able to scale team and infrastructure
- 4. Outline 1. What are Chatbots and when to use them 2. Who is doing Chatbots and how 3. How we approach it at enfore (Business) 4. enfore Chatbot implementation details (Engineering) 5. Machine learning overview and what we use (Data Science) 6. Conclusion B E DS
- 5. What are we trying to do here? Open Domain • General questions about anything • Little practical value • Quickly devolves (see Microsoft Tay) Closed Domain • Follow some agenda • Could replace humans on 1st Tier Support • What we are interested in Machine Dialogue B E DS
- 6. An Alternative Interface Is String-in-String-out a good interface for what we’re trying to achieve? Linear tasks - better off with forms • Complex questionnaires • Multi-page forms Exploratory tasks - may benefit from chatbots • FAQ - Bot could detect differently worded questions • Recommendations Do not pretend you are a human! Chatbot Text Text B E DS
- 7. Current State of the Art Amazon Alexa, Apple Siri, Google Assistant • Same thing wrapped with Text to Speech • Inspired our terminology (Intents, Utterances) • Often started out as complex question trees • Have enormous datasets to train on Other 140 Chatbot related startups on VentureRadar Can fulfill basic commands but, not much more Chatbot Text Text Text to Speech Voice Voice B E DS
- 8. What do we expect from our Chatbot? Understand requests in English, in a narrow set of domains Be able to be customized by our customers • Must be generic enough to accommodate future use cases Use cases: • Opening Hours • Product Search • Restaurant Availability • Hotel Room Availability • More to come … B E DS
- 9. What we have implemented? Intent classification • Customer intents • Help, Debug, Confirm, Cancel, Greet intents Parameter extraction (multiple at once) • Dates, Times, Durations, Integers, Locations Parameter inference • Arrival Date + Duration = Departure Date Validation • Departure date > Arrival Date • Min Duration 3 days Information retrieval Intent Confirmation B E DS
- 10. High Level Architecture NLP Service • Classify Intents • Extract Dates, Integers, etc. Chatbot • Customer Logic • Intents • Actions • Parameters Webservice Chatbot DB NLP Service B E DS
- 11. Actor Responsibilities ★ Supported Intents ★ Trigger Utterances ★ Localizations ★ Personalization Personality Customer ★ DB Interface ★ Action Interface ★ Parameter Interface Data Application ★ Maintain conversation ★ Translate requests into DB queries ★ Retrieve information Conversation Chatbot B E DS
- 12. Chatbot Workflow Intent Classification • Using Dynamic, customer-specific intent list • Handle previous intents Parameter Extraction • Remembers the parameter it last asked about • Attempts to greedily fill as many params as possible Replies • Asks about the next unfilled parameter • Responds with information Parameter inference Action Execution Intent Classification Parameter Extraction Response Formulation Chatbot B E DS
- 13. Intent Classification Objectives • Given a list of intents and query text, choose the best matching one • If none matches well enough, don’t choose any • Intents evolve over time, so be prepared to handle that Simple Methods • Naïve Bayes BoW • POS Matching Deep Methods • RNN, LSTM, Convolutional NN • Word Embeddings, Clustering • VAE, GAN B E DS Intent Classification Parameter Extraction Response Formulation Chatbot
- 14. Parameter Extraction CoreNLP • Java Library from Stanford • Big and relatively slow (1,5 GB JAR) • Tokenization, Lemmatization, POS Tagging, NER Extraction, Date and Time Parsing Dates • Always anchored around today • Tomorrow, Next Monday, Two days from now Durations • 2 hours, 1:35 Numbers • 5 people, 2 tables B E DS Intent Classification Parameter Extraction Response Formulation Chatbot
- 15. Response Formulation Are there parameters with missing values? • Ask using customer-defined questions • “When would you like to arrive?” Did we understand the last query? • Chatbot asked about arrival date and the User said “5 people” • Unable to parse some representations • Unable to identify intent Information Retrieval • “Here is our availability for next week:” Intent Confirmation • “Are you sure you want to make this reservation?” B E DS Intent Classification Parameter Extraction Response Formulation Chatbot
- 16. Ideas for the Future Improve intent classification • Use ideas mentioned in Deep Methods Intent Classification in production • Skip-thought, Sentence-to-vec, CNNs Implement ratings and feedback • Let the user provide feedback after an intent was completed • Useful for future training Make it appear more human • Sense the tone of the person • Add more polite chit-chat abilities 💡 B E DS
- 17. Conclusion Think long and hard before deploying a chatbot • Some use cases are better served traditionally • Only use in Closed Domain Simple solutions get you 90% of the way • Naïve Bayes and simple rule-based solutions work well • Use them to prototype the rest of your system Watch the Deep Learning developments • In 2016: Google’s zero-shot machine translation • In 2017: Tensorflow Fold and Tree-based NNs 🤖