Category: Chat

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Have you made any recent flight bookings from any leading travel site? If yes, after booking, you might have received a confirmation and ticket to your WhatsApp from the area along with the number and details which a chat application bot is doing!

Interesting, isn’t it? This is how businesses simplify their workflows using chatbots without human intervention at every step of the process.

Does your business rely mainly on communication and collaboration?

How about using the messaging platforms or chatbots to accelerate the workflows?

The messaging and collaboration platforms have become one of the key accelerators for productivity within the teams. If most of the activities in your business are collaborative, you need simple apps built with a top messaging framework. Here are the top 5 best chatbots that boost workflow management and process automation to the next level.

1. Conversational chatbots

The conversational AI bots can mimic human conversations almost accurately and engage with people without pre-scripted responses. By using Artificial intelligence, processing, and technology, these Chatbots can make personalized and contextual conversations.

Example

With Quickwork Conversations, you can build multi-channel communication in mind and connect with any messaging platform like a pro. It manages the discussions for the businesses and helps in making a better customer journey.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

2. Customer service chatbots

By using the Chatbots in the customer service department of your business, the bots can help you with a wide array of tasks. From self-service to answering a set of questions, they can answer it right without the need for human intervention.

Some examples of customer service chatbots

Welcome Bots: Not all customers are comfortable initiating the conversation so that a proactive bot can set you apart.

Resolution Bots: this kind of bot can resolve over 80% of the customer queries quick and fast

3. E-commerce bots

Online Ecommerce is like an ocean where you can find a sea of pages, crosslinks, and more. The Ecommerce Chatbots streamlines the procs for customers and provides a great shopping experience. Here are some types of Chatbots that can employ Ecommerce service bots.

Examples

Sales bots — The bots that automate the sales and the conversations related to the sales processes like providing price lists and executing the sales. It helps to boost your revenue and improve the sales strategy and approach.

Billing bots –The billing process is tedious, and several monotonous tasks are involved in the billing process, which is exhausting to the sales teams. A billing bot can replace your team in this process.

4. Lead generation bots

The lead generation tools are not new, as they have been employed for years by the marketing people. Now, even the Chatbots are capturing and nurturing the leads. They can be more proactive, trigger based on the conversations.

Examples

Conversation bots — The Chatbots establish the conversations in the most precise manner and helps in capturing and communicating with the leads.

5. Survey bots

Customers are not interested in filling the surveys and answering all the questions online. Deploying Chatbots to capture your customer data and surveys from the people is the intelligent approach today, and it nestles the questions without a conversation.

Feedback bots — If a customer purchases some goods from your website, a feedback survey can help you improve the customer service and experience. These bots can make a survey process less tedious compared to manual ones. The bots interact with the customers in real-time and help businesses seamlessly capture the feedback.

Summing it up

The above top five chatbots are the best type of bots that can improve your operational efficiency, workflow management and helps in automating your complex tasks.

If you want to build chatbot journeys and integrate multiple applications using the best workflow automation platform, Quickwork is the best choice. Start building your first automation journey with Quickwork today!

Don’t forget to give us your 👏 !

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Top 5 chatbots for workflow management and process automation was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In our previous article, we talked about what universal design for learning (UDL) is and explained its key principles.

In this article, we’ll tell you why lecturers should start using universal design for learning ASAP. If you’re already interested in incorporating UDL into your classroom, we also explain how you can do that.

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Mercury — a chatbot for Ordering Food using ALBERT & CRF

Unless you have been out of touch with the Deep Learning world, chances are that you have heard about BERT, ALBERT and CRF (Conditional Random Field).

Mercury, named after the Greek God Hermes who was the messenger of the Gods, is a chatbot service which can be integrated with various Food-Delivery brands such as Swiggy or Zomato where a User can simply type in his order and send it as a text.

Mercury can then extract the essential information from the order and place the order for the User accordingly.

Here is a list of technologies involved in Mercury :

=> ALBERT (which looks like BERT++)

=> CRFs (Conditional Random Field)

=> gRPC (Google Remote Procedure Calls)

=> JointALBERT Slot-Filling & Intent Classification

=> Flutter (Front-End)

Since this paper is about “Mercury”, I will only be providing a brief summary and some useful links for a more in-detail understanding of each concept.

What is BERT?

Let’s take a look at these sentences where the same word has different meanings :

|- I was late to work because I left my phone at home and had to go back.

|- Go straight for a mile and then take a left turn.

|- Some left-winged parties are shifting towards centralist ideas.

How do you differentiate between each meaning of the word left?

These differences are almost certainly obvious to you, but what about a machine? Can it spot the differences? What understanding of language do you have that a machine does not?

Or rather, What understanding of language do you have that machines did not have earlier?

The Answer : Context.

Your brain automatically filters out the incorrect meaning of words depending on the other words in the sentence, i.e. depending on the context.

But how does a machine do it?

This is where BERT, a language model which is bidirectionally trained (this is also its key technical innovation), comes into the picture.

This means that machines can now have a deeper sense of language by deriving contextual meaning of each word.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

What is ALBERT?

ALBERT was proposed in 2019, with the goal of improving the training and results of BERT Architecture by various techniques :

Parameter Sharing (Drop in Number of Parameters by over 80%)| Inter-Sentence Coherence Loss | Factorization of Embedding Matrix

Results of ALBERT on NLP benchmarks :

What are Conditional Random Fields?

CRF classifies inputs to a feature from a ‘list of potential’ features.

I will be going into a little more detail shortly but for now, just understand that CRFs are used for predicting sequences depending upon previous labels in sentences.

They are often used in NLP in various tasks such as Part-Of-Speech Tagging and Named-Entity Recognition since CRFs excel in modelling sequential data such as words in a sentence.

What is gRPC?

It is an open-sourced high performance Remote Procedure Call framework.

It’s main advantage is that the client and server can exchange multiple messages over a single TCP connection via the gRPC Bidirectional Steaming API.

Mercury uses gRPC bidirectional streaming API for implementing Speech-To-Text functionality by using the Google Speech-To-Text API.

Mercury — What’s under the Hood?

What does Mercury do before placing the order for the User?

How does Mercury know that the text it has received is indeed a request for placing an order?

Let’s take a look at this sentence:

“I would like to have 1 non veg Taco, 3 veg Pizzas and 3 cold drinks from Domino’s.”

How does Mercury go from this to something like this?

This is where Joint-ALBERT (Slot-Filling & Intent-Classification) comes into the picture.

Sneak-Peak under the hood of Mercury’s Model

Training :

We come up with some desired labels for our model.

Intent Label : <OrderFood>

Slot Labels : <restaurant_name> , <food_name>, <food_type>, <qty>, <O> (<O> means that specific word does not carry much value in the sentence and can be masked or ignored).

We create hundreds of sample sentences with labels associated to each word.

ALBERT + Conditional Random Field (Joint-ALBERT):

We have already learnt that CRFs excel in modelling sequential data. So how does it help Mercury?

CRFs essentially help in mapping each word to it’s appropriate label.

For example:

It can map the number “1” to <qty> denoting quantity.

It can map the word “Domino’s” to <restaurant_name>.

Great! So if CRFs can do this, why do we even need ALBERT?

In our original sentence :

“I would like to have 1 non veg Taco, 3 veg Pizzas and 3 cold drinks from Domino’s.”

How does CRF know that the word “non” is a <B-food_type> and the word “veg” is <I-food_type> (B means beginning & I means continuation of B)?

How does CRF know that the word “non” is not the dictionary meaning “anti”?

As you probably already guessed, ALBERT provides CRF the contextual meaning of each word which helps CRF in classifying each word into the correct slot labels.

CRF does Slot-Identification for each word by mapping each word’s possible label with each other and figuring out which mapping has the highest probability.

Finally, how is Intent of the sentence predicted?

CRF does this part too by figuring out that “A specific sequence of slot-labels leads to a specific Intent”.

For example :

If the slots <food_type>, <food_name> and <restaurant_name> are found in a sentence, then the sentence is probably having the intent of <OrderFood>.

Flutter Front-End

Mercury has a simple and elegant front-end for the User.

Some Useful Links :

You can watch a quick 3-minute Demo of Mercury on My Mercury Website.

You can also check out my other projects on My Main Website.

BERT Paper: Here is the arxiv BERT Research Paper.

ALBERT Paper: Here is the arxiv ALBERT Research Paper.

CRF Paper: Here is the arxiv CRF + LSTM Research Paper for Sequence Tagging.

JointBERT: Here is the arxiv JointBERT (Intent Classification and Slot Filling) Research Paper.

gRPC Introduction: This will get you started with gRPC Basics.

adios, amigos!

Don’t forget to give us your 👏 !

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Mercury — A Chat-bot for Food Order Processing using ALBERT & CRF was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.