Category: Chat

IBM Watson Assistant provides better intent classification than other commercial products according to published study

And how the better performances of IBM Watson Assistant tackle the most common pain points in the adoption of a conversational solution

Introduction

Today, virtual assistants (or chatbots) represent a globally recognised booming trend:

With regards to chatbots, which are in many ways the most recognisable form of AI, 80% of sales and marketing leaders say they already use these in their CX or plan to do so by 2020¹.

25 percent of customer service operations will use virtual customer assistants by 2020².

By 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25%³.

It is clear that chatbots are here to stay.

Their limitless potential in terms of business versatility — is there any business that does not implement customer service operations? — , maturity of technologies, and absolute clarity of matching use cases and expectations, make the virtual assistant one of the most immediately adoptable A.I. solution in any company.

Nevertheless, if you have ever used a chatbot, you may have experienced the frustration coming from the reiterated invitations to rephrase even apparently simple questions (“Sorry, I do not understand.”), and a slight, yet non-negligible, sense of distrust of any virtual assistants’ comprehension abilities matured from firsthand experience.

In fact, it is also estimated that:

40% of chatbot/virtual assistant applications launched in 2018 will have been abandoned by 2020³.

There are several conversational technologies that could be adopted when implementing a virtual assistant. Nowadays, every major Cloud vendor provides a conversational API infused with A.I. capabilities in its service catalogs. To cite a few:

• IBM Watson Assistant⁴
• Google Dialogflow⁵
• Microsoft LUIS⁶
• Amazon Lex⁷
• Oracle Digital Assistant⁸
• . . . and more

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It is also possible to adopt open-source conversational services (such as RASA⁹) or even create your own by leveraging publicly available Natural Language Processing (NLP) projects, like BERT¹⁰.

This post highlights some of the advantages of IBM Watson Assistant in comparison to other commercial tools, based on the evidences reported in the recent technical paper by Qi et al. (2020)¹¹. In doing so, we will start by the most common pain points that occur with the adoption of a virtual assistant, and comment how the study findings address them.

The chatbot does not perform well in terms of understanding

Intent detection, the classification of the user’s utterance into a predefined class (or intent), is the solid pillar upon which any task-oriented conversational system is built.

An example of intent in the banking field may be “Card Lost”, as shown by Casanueva et al. (2020)¹² :

The classification of an input sentence as an intent allows the conversational service to identify the reply for the user, through an existing mapping between the intent label and a node in the conversational tree. Therefore, the intent misclassification may lead to either wrong replies or no reply at all (“Sorry, I do not understand.”). Understandably, intent’s misclassification represent the first point of failure and the nightmare of any conversational solution.

IBM Watson Assistant provides a new intent detection pipeline that leverages complex Machine Learning algorithms and methodologies — namely transfer learning, AutoML and Metalearning¹¹.

The study by Qi et al. (2020)¹¹ shows that IBM Watson Assistant outperforms other commercial solutions in terms of accuracy in the task of intent detection:

The same benchmark and testing methodology as Arora et al. (2020)¹³ were used. In particular, Arora compared Google Dialogflow⁵, Microsoft LUIS⁶, RASA⁹ and BERT¹⁰ with Haptik¹⁴. As IBM Watson Assistant was not considered in the first place, Qi et al. (2020)¹¹ used the same data sets and experimental setup to add it to the benchmark and obtain comparable results.

According to the study, IBM Watson Assistant shows higher accuracy than than Google Dialogflow (+5.6%), and Microsoft LUIS (+14.7%), on the same data set and in the same testing conditions.

How many sentences do I have to add to make the chatbot work well?

When planning for the adoption of a conversational solution, an important concern is about the effort required to train and maintain it.

Despite the presence of established approaches to the measurement of fair performances, and the availability of different performance metrics, the fear that no training data set will be “complete” enough to work reasonably on a real scenario may be blocking.

Frequent questions involve: “How many utterances do I need to train the system properly?” or “How many people should be assigned to the task of manufacturing training samples?”

Qi et al. (2020)¹¹ also tested the technologies on smaller data sets (“Subsets”), created by discarding semantically similar sentences from the original data sets (“Full”), to investigate the behaviour of the models with reduced training set size:

According to their study, not only IBM Watson Assistant outperforms other commercially available products, but also displays the smallest drop in accuracy when the size of the data sets decreases, possibly making it a better choice in scenarios with a minor amount of input data.

Adding new examples/intents and re-training is prohibitive

On the surface, the implementation of a chatbot may seem mainly a technological matter. But it is not.

Indeed, the adoption of a virtual assistant requires significant operational adaptations, as periodic trainings must be taken into account. Although this happens for every Machine Learning project, for chatbots — unlike in the case of labeled, clean tabular data— the definition of new intents, relevant training utterances, the segmentation of the knowledge base to manufacture suitable replies, need the infusion of specific business knowledge, the support of a Subject Matter Expert (SME) and, for most cases, is not an automatic process.

An example: a company launches a new product into the market; as a consequence, users will ask new questions concerning the product to the virtual assistant. The SMEs should be involved to identify the potential questions (they may also overlap with already existing ones) and manufacture adequate replies.

For these reasons, a company would need a conversational service:

1. easy to train: user-friendliness fosters business adoption.
2. that trains rapidly: quicker and simpler training sessions result in smoother enhancements of the knowledge base.

The lack of one of these conditions may result in the failure to adopt the solution and its abandonment in the long run.

Both conditions are met by IBM Watson Assistant, that provides an extremely intuitive graphical user interface that does not require technical knowledge to be effectively employed.

Qi et al. (2020)¹¹ measured the training time required by different Natural Language Understanding (NLU) engines in order to process the same input data set, and IBM Watson Assistant offered the best trade-off between final accuracy and training time:

Who is going to use the tool?

The user-friendliness of the tool is not object of investigation in the cited study, but it is important to mention that, as IBM Watson Assistant is easy to use and does not require a strictly technical skillset, it encourages its adoption by business users.

Business users alone possess the specific domain knowledge and peculiar jargon to be imbued into the chatbot. Empowering them with a friendly instrument that allows easy modifications is paramount to the success of a conversational solution.

Conclusions

In this post, we commented the results of the paper from Qi et al. (2020)¹¹, which, by comparing different conversational services from multiple vendors, shows that IBM Watson Assistant displays generally better performances, in terms of:

1. Higher intent detection accuracy.
2. Minor drop in accuracy with less input data (smaller training sets).
3. Best trade-off between accuracy and training time.

We also remarked how these results address the most common pain points that appear with the adoption of virtual assistants, together with the user-friendliness characterizing the tool.

This post does not intend to provide an exhaustive overview of the differentiating capabilities offered by IBM Watson Assistant, such as its irrelevance detection features¹⁵ or its native integration with search services¹⁶ (namely IBM Watson Discovery¹⁷) to better support navigation through large document corpora within the chat and manage questions in the “long-tail” . This goes beyond the scope of this article.

References

[1] Oracle, “Can Virtual Experiences Replace Reality?”, link.

[2] Gartner, “Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020”, link.

[3] Gartner, “Market Guide for Virtual Customer Assistants”, link.

[4] https://www.ibm.com/cloud/watson-assistant

[5] https://cloud.google.com/dialogflow

[6] https://www.luis.ai/

[7] https://aws.amazon.com/lex/

[8] https://www.oracle.com/chatbots/digital-assistant-platform/

[9] https://rasa.com/

[10] Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, 2019, arXiv:1810.04805, link.

[11] Haode Qi, Lin Pan, Atin Sood, Abhishek Shah, Ladislav Kunc, Saloni Potdar, “Benchmarking Intent Detection for Task-Oriented Dialog Systems”, 2020, arXiv:2012.03929, link.

[12] Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, Ivan Vulić, “Efficient Intent Detection with Dual Sentence Encoders”, 2020, arXiv:2003.04807, link.

[13] Gaurav Arora, Chirag Jain, Manas Chaturvedi, Krupal Modi, “HINT3: Raising the bar for Intent Detection in the Wild”, 2020, link.

[14] https://www.haptik.ai/

[15] https://medium.com/ibm-watson/enhanced-offtopic-90b2dadf0ef1

[16] https://medium.com/ibm-watson/adding-search-to-watson-assistant-99e4e81839e5

[17] https://www.ibm.com/cloud/watson-discovery

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IBM Watson Assistant provides better intent classification than other commercial products… was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Chatbots in Healthcare Boost Access to Care During COVID-19

Shortening of the English “chat” to converse and “bot” (robot), a chatbot is similar to a conversational agent. Concretely, it is a machine program, designed most of the time to perform a defined task. Its particularity lies in its capacity to interact in natural language with the user. Because unlike other electronic dialogue tools such as interactive voice servers, the chatbot enables them to show themselves more or less freely through a text or voice interface or sometimes even hybrid.

A chatbot with a textual interface will principally be obtainable from a smartphone or a computer. It can be a stand-alone appliance or integrated with a mobile app or a website. Communicating with “it” is very much like an SMS transfer, usually coupled with pre-defined question-and-answer series, in the case of a voice interface, which is described as a voice bot or voice helper. The user interacts by voice once the popular wake-up-word has been spoken to activate the program — the products produced by GAFAM such as Alexa from Amazon or the Google Assistant.

To give some historical benchmarks, which produced the very first chatbot in 1966. It is ELIZA, which we owe to MIT professor Joseph Weizenbaum. By reformulating the user’s messages in the form of questions, the program is designed to simulate a psychotherapist’s intervention. It should be noted in passing that the tale of chatbots is therefore closely connected to the health sector, and more particularly, to psychology. But the technology did not develop until the mid-1990s. In 2010, it became accessible to the general public with the very original voice assistant: Siri, integrated into Apple’s IOS. The other digital giants have immediately followed suit, and since 2016, we have observed a wild development of conversational technologies, all areas connected.

A market helpful to the development of conversational technologies

Fruit of technological development in the fields of automated natural language processing and artificial intelligence in special Machine Learning, based on machine learning algorithms, and Deep Learning, deep learning based on artificial neural networks, the chatbot is starting a phase of “sophistication.” It is slowly moving away from the simple outlines built-in decision trees that marked the origins of its development.

Its current success is inseparable from the growth of interfaces and other platforms hosting chatbots. Facebook’s Messenger Bot Store is the most impressive example because they make marketing less costly and, above all, less complex by making it feasible to do without computer coding.

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Also, the combination of chatbots at the heart of immediate messaging services makes them quickly accessible to billions of users every day. WeChat, Slack, Facebook Messenger, WhatsApp, or even Telegram, which today total no less than 5 billion active users per month, more than social networks.

It is this favorable ecosystem that mainly describes the quantitative and qualitative improvement of the chatbot offer. The user finds a chatbot device particularly adapted to the new hyper-connected lifestyles that value immediacy, communication, and unprecedented ease of access compared to mobile purposes or websites.

Robot Assistants

Not to mention that, as recognized by researchers, individuals show themselves willing to speak to robot assistants longer than to human beings and are also more inclined to share their most intimate confidences with them due to the absence of fear of judgment. If you want the best cure for impotence then take Fildena 100 or Cenforce 100. Giving real-time, personalized, and more “human” interaction, usually increased through users’ feedback, conversational communication is more favorable to engagement.

Therefore, it is simpler to understand that many investors and entrepreneurs from various sectors are involved in e-commerce, insurance, transport, etc. Hence, brands are investing heavily in conversational selling.

It looks that in health, this technology has developed more slowly than elsewhere, surely because of the highly organized nature of the sector and because of the chance involved. However, health is not left out because the organization and automation of jobs and requests are, too, a major problem.

The healthy chatbot: who, for whom, and for what?

The use cases in France are, for the time, few. It is why our mapping of real solutions extends beyond our borders, without any claim to be exhaustive.

What should note that currently, chatbots are overwhelmingly designed for patients’ potential or proven? Among the tools offered to healthcare professionals, let us quote pharmacology chatbots such as Posts and Synapse Medicine, designed by two French startups. Finally, some solutions are sometimes aimed at giving a free version for the general public and a paid version for professionals: this is the case of a North American chatbot training in women’s health, Dr. Chat.

Two distinct ways of cataloging use

In the answers offered to the common public and patients, the service given is fundamental, under penalty of seeing users lose interest instantly because of a need for relevance.

A first arrangement makes it possible to identify general chatbots from masterpiece chatbots.

Among the GENERALIST CHATBOTS, we can mention:

Sign checkers such as Buoy, MD, Gyant, SENSELY, or INFERMEDICA. Numerous and mainly given in North America and the United Kingdom, this kind of chatbot asks the patient a series of questions that provide a list of possible causes or conditions to be drawn up as they become more apparent. This pre-consultation stage can precede a practitioner’s position, or even a teleconsultation, or, in the case of good pathology, give self-care choices.

Like Florence Chat, personal health partners help patients achieve their treatment by reminders and alarms and encourage them to stick to their treatment protocol. Mabu, the health guide for seniors developed by IDEO and Catalia Health, also collects data on the patient’s progress and can alert their medical team when required.

The triage chatbot is programmed to deliver medical advice to optimize an emergency admission or telephone assistance service load. This is the case of a test conducted by the National Health Service in north London on a pool of more than one million people.

Incurable disease tracking chatbots are also very common. They are a member of a dual logic of observation and monitoring. Diabetes is a blood glucose monitoring tool for diabetic patients available in two versions, adults and children. CardioCube is a voice assistant that helps patients manage their chronic heart disorder and communicates in real-time with the hospital or clinic. They depend on facilitating and stimulating decision-making when required.

Oncology is an area in which chatbots give exciting prospects, mainly due to the collection of real-life data and the need to help patients and caregivers throughout therapy.

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Individual implementation of chatbots in healthcare was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introducing bot templates for websites an easier, faster and fully flexible way to realize the full website’s potential and provide a better customer experience in the right place and at the right time.

Here at Botmakers, we believe that the right chatbot is an indispensable colleague for any business and that chatbots work not only for messaging platforms. Introducing WEB BOTS: simple, professional, high-conversion and code-free bot on your website that you can customize exactly as you need.

What are the benefits?

Self-sufficiency – web bots are free from Facebook and its Policy. In other words, they give freedom to website visitors from signing in with a Facebook account in order to interact with a bot (that’s what standard Messenger widgets installed on the site can’t brag about).

That means you significantly save your potential customer’s time which positively affects your conversions.

Imagine those who don’t have a Facebook account at all. In that case, a web bot might be your saving grace by letting visitors interact one-on-one with a bot on your website.

P.S. Facebook constantly changes Messenger platform privacy policies. You have to follow and keep your bot up-to-date otherwise your Business Facebook account might be blocked.

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1. 3 Tips for your Voice and Chatbot Program from Gartner’s Customer Service Hype Cycle 2020

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Why use website bots?

The average conversion rate on a website generally sits in the ~1% range. One of the most effective ways we see web bots being used is to help potential leads who might not have converted without chat. Sometimes a quick question or a proactive offer to help can be the difference between average conversion rates and filling your pipeline full of qualified leads.

Another effective way of using web bots is to generate more qualified leads. With the help of a bot on your website, you can easily share valuable content with your website visitors. As an example, you can use a bot instead of a landing page and share your e-book, a PDF, or a coupon through the bot once a lead has given their email.

How web bot can fuel your website and your business?

1. Personalize the customer’s experience

• Web bots give an opportunity to identify website visitors and personalize their experience, that’s what chat widgets can’t cover as well.
• Using web bots you can have a specific bot on a specific website page.
• Web bot can deliver the right message for every visitor and every web page to engage them in a personalized way.
• You can see who your visitors are, where they came from, and how they interacted with a bot.

2. Get full control of your web campaigns

• With web bots, you can filter your campaigns by page URL.
• Filter by location – country and/or city.
• Filter by goals and completed campaigns.
• Filter your campaigns by referring websites.

3. Streamline your campaign performance

Web bots provide analytics so you can optimize your bots for high conversion rates with no hassle.

• Real-time reporting
• Campaigns and goals performance
• Conversion over time
• Impressions and Engagement charts

There are a couple more benefits of using website bots:

Creating a website bot is fast and the results are in. There’s zero programming in website bots to learn. The best bit? First web bot templates are already available with your Botmakers subscription! They are completely customizable, so you’re in control and can adapt your bot exactly as you need.

So, if you plan to use your bot for onboarding, collecting data from customers, collect payments, supporting or engaging in real-time – Website Bot is a good marketing investment!

We hope you’re as excited about website bots as we are! Get started with your Botmakers plan and get full access to all Botmakers Templates for Website (more and more templates are coming soon) and Facebook Messenger.

About the author

Eugene Smirnov is the Head of Templates Production at Botmakers with 3 years of experience in the business.

Contributor at Chatbots Magazine, Chatbots Life, Chatbot Tutorials chatbot media.

Eugene eventually reads every response on Medium. You can chat him up on LinkedIn or email at eugene@botmakers.net if you have any related questions.

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Website Bots: A New Template Category Launches On Botmakers was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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