October 28

Rakuten Technology Conference 2017

Laguido Nio

NLP Researcher and Data Scientist

Rakuten Institute of Technology

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Lasguido Nio

More about me:

www.sigmo.id

o PhD on Dialog System and

Natural Language Processing

• Specialized in End-to-end

Chat-oriented Dialog System

o Past collaboration:

• Join research with A*star I2R Singapore

• Join research with Sony China Research Lab.

o NLP Researcher and Data Scientist

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…

For Developers:

• Learn about the conversational-bot components and some of its tools

For Researchers:

• Understand the conversational-bot components and possible future research

For You:

• Understand how AI works behind the Conversational-bot

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Conversational Bot / Dialog System

Chatter-Bot / Chatbot

“Computer program that has a certain goals, and able to

interact or conduct conversation with human via

auditory or textual method”

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“bots are the new apps”

- Microsoft CEO, Satya Nadella -

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1. Advancement in Artificial Intelligence

2. People enjoy conversational interface

“People spend more than 4 hours per week

in communication apps” – Nielsen*

3. Cheap labor: customer contact center

*http://www.nielsen.com/us/en/insights/news/2016/got-a-minute-how-our-use-of-communication-apps-changes-by-the-hour.html

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Based on the dialog purpose:

• Goal-oriented

(E. Seneff et al., 1991; Walker et al., 2000)

• Chat oriented

(J. Weizenbaum et al., 1966; Wallace et al., 2003; Nio et al. 2017)

“I am looking for

cheap red clothes“

“Am I look good

with red clothes?“

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Based on how to give a response:

• Rule based (Wallace et al., 2003)

• Data driven/statistical (Young et al., 2013)

database

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Based on its architecture:

• Modular-based System

• End-to-End System

• End-to-end chat-oriented bot

(Vinyals et al., 2015; Shang et al., 2015; Serban et al., 2015; Nio et al., 2016)

• End-to-end goal-oriented bot

(Wen et al., 2017; Chen et al. 2017)

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Artificial Intelligence, but why?

Data driven (statistical) approach

Doesn’t rely much on domain expert

Scalability

Here we need a lot of training data

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• Natural Language Understanding NLU

• Dialog Management DM

• Natural Language Generation NLG

NLUDialog

Manager

NLG

you

I want to eat miso ramen.

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NLUDialog

Manager

NLG

I want to eat miso ramen.

Classify user sentence into frames and intent

(Chen et al., 2016; Hakkani-Tur et al., 2016)

Widely available chat-bot tools already implements this

function

sent: I want to eat miso ramen.

frame: type food

intent: find_food

you

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NLUDialog

Manager

NLG

Dialog Manager composed by two component:

• State tracking: Determined the state of conversation

(Williams et al., 2012; Henderson et al., 2015)

• Policy learning: From the learned policy,

determined the next action (Su et al., 2016)

acquired: {type: miso, food: ramen}

action: ask_location

Most chat-bot tools manage it’s dialog manager by using a decision-tree-like

architecture

you

sent: I want to eat miso ramen.

frame: type food

intent: find_food

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NLUDialog

Manager

NLG

Where do you like to

have your ramen?

Realizing sentence output from frames (Wen et al., 2016)

This is something that yet not available in the existing chat-bot tools

you

acquired: {type: miso, food: ramen}

action: ask_location

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…

chatbot

I want to eat miso ramen.

Where do you like to have your ramen?

you

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…NLU

DialogManager

NLG

…

chatbot

I want to eat miso ramen.

Where do you like to have your ramen?

you

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• Most available tools only support NLU

• To generate the response, you need to build the dialog decision tree

• You also need to provide (a lot of) the response template

(query-response template)

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Hi, this is a restaurant finder robot. What type of food are you looking for?

I want to eat ramen. I would like to eat curry, looking for budget restaurant.

Which one do you like miso, shoyu, shio, or tonkotsu ramen? Do you want me to find curry restaurant near Tokyo?

… …

• A decision tree-like structure

• Requires domain expert to identify topics

Bot Agent

User

frame: {food-type: ramen}

intent: tell_preferences

frame: {food-type: curry,

price: budget}

intent:tell_preferences

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Built-in

AI NLU

User

Interface

Web Builder

SN

Platform

Support

Built-in

Analytics

Tool

Built-in

Speech

Function

Notes

Microsoft Bot

Framework

+ For developer

+ AI with LUIS (Language Understanding

Intelligent Service)

+ Open source

IBM Watson

+ For developer

+ Watson AI Toolkits

MOTION.AI

+ For bot builder

+ Easy to learn

+ Drag & drop interface

ChatFuel+ For bot builder

+ Free

+ Drag & drop interface

DialogFlow

Google Bot

Framework (API.AI)

+ For developer

+ Pre-built dialog template

BotPress

HubSpot

(as 20 Sept. 2017)

+ For developer

+ Open source

*There are hundreds of chat-bot tool out there, these are only some of them

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