www.womenindata.co.uk #womenindata JCURV 1 Chatbot for social good

www.womenindata.co.uk #womenindata  JCURV 1 Chatbot for social good

www.womenindata.co.uk #womenindata JCURV 1 Chatbot for social good and NLP in the wild

A little bit about me I am a Data Scientist at AI for Good UK working to improve product intelligence and communicate findings with wider stakeholder groups and the founder of the AI for Good community here in London JCURV

3 In this talk I am going to go through... Survey of chatbot technologies Main applications and some case studies of success and failure The current state-of art and the progression to such What is the future direction of this field

rAInbow our chatbot and the problems that we encountered The story behind rAInbow - why is it an innovative solution NLP work and progress Future directions AI for Social Good

JCURV 4 What is a chatbot? NLP papers classify Conversational AI into three categories: question-answering agents, task-oriented dialogue agents and chatbots.

Wikipedia says: A chatbot is a piece of software that conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, although as of 2019, they are far short of being able to pass the Turing test. Source: chatbot magazine JCURV

5 Turing Test Source: BFI JCURV

6 Some prevalent use cases of chatbots By Business Functions: Customer Service Marketing Sales HR

JCURV By Industry: E-commerce Travel Finance

7 Successful Chatbot Applications Social Chatbot Legal Chatbot Source: Nautilus | Science Connected, Crunchbase, Google Play

JCURV Mental Health Chatbot 8 Tech Stack behind Chatbot: What makes them intelligent?

Some Natural Language Processing (NLP) Lingo Intent: intentions of the end-user, these intentions or intents are conveyed by the user to your bot e.g. greeting,book_appointment Entity: person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages. Named-entity recognition is the technique to automatically identify these entities in free text. Embedding: vectors, e.g. word embeddings

RNN, LSTM: Source: Towards Data Science Lawful Good chatbot architecture Source: Chatbot Magazine

True Neutral chatbot architecture Source: https://dzone.com/articles/understanding-architecture-models-of-chatbot-and-r Chaotic Evil chatbot architecture Source: https://dzone.com/articles/understanding-architecture-models-of-chatbot-and-r

Generative model in the wild Source: Engadget XiaoIce - a social chatbot Wouldnt be possible without advancement in NLU, Speech Recognition and Synthesis, Computer Vision, Information Retrieval, Multimodal intelligence and empathic conversational system.

Requirement of a social chatbot: Understanding users, Interpersonal response generation, Personality, Integration of both EQ and IQ Source: https://arxiv.org/pdf/1801.01957.pdf XiaoIce - continued Source: https://arxiv.org/pdf/1801.01957.pdf

Neural Approaches to Conversational AI Source: https://arxiv.org/pdf/1809.08267.pdf Neural Approaches to Conversational AI Source: https://arxiv.org/pdf/1809.08267.pdf

Neural Approaches to Conversational AI Source: https://arxiv.org/pdf/1809.08267.pdf Meanwhile standard industry toolbox -True Neutral Source: pickSaaS, WikiPedia


I know you do this with no thought of praise or reward, but allow me to offer my own recognition of the innovative solutions you have helped to create with the power of AI. From providing mental health

support to adolescents in low income countries, to confidential chatbots that signpost support services to hundreds of thousands of women at risk of domestic abuse. Research phase - South

Africa Stigma, shame, victim blaming A nonjudgemental companion

Barriers to accessing support Lack of trust in support system

Need 24/7 support User-friendly experience and support

Hi Kriti, Im Rainbow, but my friends call me Bo Im here to talk about relationships that dont feel right. This is how youd interact with me

rAInbow user insights 350,000 conversatio ns in 8 months (Nov 2018)

6-10AM and 7-8PM peak hours on rAInbow 9AM to 5PM helpline hours Saturdays 10%

increase Sundays 15% increase Monday to Friday in conversations helpline days

Better Data Better Lives Accurate data is the lifeblood of good policy

and decision-making. - UN Secretary-General Antnio Guterres 350,000 conversatio

ns with 14,000 users Data Science insights Engaged

conversations average 60 messages average 20 minutes more than half returned What users want

personal atconversations their own pace in their own time My relationship doesnt feel right how most people describe

abuse abuse, violence, rape how services are advertised Users who engage with Bo

through AI-powered personalised stories are 40% more likely to take action than those who dont

NLP Research Challenges English proficiency of users - a variety of grammar and spelling - off-the-shelf tools do not work Specific domain - less data - low transferability

NLP Research Current Progress Unsupervised learning can only carry you so far - no free lunch in NLP Tagging is difficult - what categories, intents, entities, sentiments to tag? NLP Research Future Directions Deep learning approaches for domestic

abuse reporting message identification to enable human handover Graph-based NLP approach to cluster sentences for new topics and themes mining A few words about AI for Social Good Meetup communities: AI for Good Community, AI for Good: Element

AI, Microsoft: Impactful AI, Tech for Good London... Companies and organisations to follow: AI for Good, Element AI, DataKind UK, Alan Turing Institute... Conferences and workshops to watch: AI for social good, AI for Climate Change JCURV



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