NLP Chatbot Resiliency: A Chat With Botpress
In the race to design great conversational experiences, adaptable NLU models will play a key role in the creation of truly intelligent chatbots. In this article, learn how Botpress stemmed from frustrations with poorly designed bots that led to a launch of an open-source managed NLU platform. Also, see why the future of chatbot design will shift from an intents-based approach toward knowledge-based models that offer greater adaptability and resiliency.
Developer Accessibility Key to NLP Chatbot Advancement
Chatbots have come a long way over the years, evolving from simple command-response models to the more nuanced NLP conversational models of today.
Looking ahead, NLP shows no sign of slowing. Some projections state the NLP market will reach $127.26 billion by 2028 (up from $20.98 billion in 2021). This is remarkable growth, but it’s also not all that surprising given current consumer demands.
Across virtually all digital experiences, consumers now expect personalization and ease. It’s here where NLP chatbots are poised to truly shine by creating natural humanistic conversations tailored to the end user’s experience. Delivering on that reality is another story altogether. It all comes down to whether NLP achieves widespread accessibility by developers.
Generally, developers have two options when building an NLP chatbot. The first is the in-house route, where developers build their bots on the back of complex software like Rasa. The benefit of going in-house is that it provides a highly customizable solution for experienced Python developers. On the flip side, however, implementation is time-consuming and requires an experienced team of developers, data scientists, and ML experts closely managing and updating their NLU models in order to successfully execute. Further, some options lack a user-friendly visual tool, making the learning curve extremely steep for non-technical users.
Another major strike against this route is that development happens in a closed ecosystem. As a result, the end product often runs the risk of being out-of-date compared to current NLP models when it actually hits the market.
The second development option is through a “buy off the shelf” solution. As the majority of the heavy development lifting is completed, non-technical teams or teams with limited NLU experience will find this option more favorable. Because development time is significantly reduced, this option provides a quicker time-to-market and requires lower overhead for staff resourcing. On the downside, little customization is available with this option making it a less favorable choice for scenarios where complex and dynamic conversation flows are desired.
And then there’s a third option: a place where developers can freely create robust conversational experiences without significant NLP expertise. This is where Botpress sits.
Botpress
Soon after the chatbot boom began to take shape and inspired by my experience building an internal bot, I launched my own chatbot builder. This time, however, I wanted to create an NLP-based solution that would be highly accessible by all developers, regardless of their NLP experience.
Thus, Botpress was born. In the conversation below with Tony Ramos and Jason Gilbert of the Conversation Design Institute Festival, hear the story of how Botpress started entirely unintentionally. We also chatted about the state of NLP chatbot design and future resiliency.
Check out my full chat with the Conversation Design Institute below.
Adaptability Is the Key To Resilient NLP Chatbots
Looking ahead, I believe resilience will be a crucial driver in NLP chatbot development. This resilience is essential given the fluid nature of NLP’s core subject matter: human language.
Language is beautiful; but, it is also nuanced, and at times, sloppy. As such, NLP chatbot build solutions need to be resilient to the nature of language. To achieve that, they must be adaptive.
One way I see the NLP chatbot build space achieving that is through a shift away from the standard intent-based approach. Intents-based models rely heavily upon a consensus from user inputs to function properly. The problem is that humans rarely consistently reach an agreement on the specific questions they’re asking a chatbot. If humans can’t reach an agreement, how can we expect computers to produce a consensus in outputs?
Intents, in my opinion, are limiting to NLP advancement in that they lack long-term adaptability.
One way we’re working to achieve adaptability as a core driver of the Botpress platform is through a shift towards knowledge-based task fields. These tasks enable developers to build bots that only extract and act upon the vital performative words within a sentence. This process also omits all unnecessary information that a user may input (including typos and contextual nuances). What remains is a clean data set that the bot can appropriately interpret and respond to as desired.
Another way we’re achieving adaptability is through the open-source nature of our platform. As an open-source solution, the contributions of Botpress users ensure our product is continuously improved. By doing so, our platform can keep in step with evolutions in NLP technology as they happen.
To learn more about Botpress’ documentation, check out our Developer’s Guide, or head over to our Community Forum to hear what other developers think of our platform.
In the race to design great conversational experiences, adaptable NLU models will play a key role in the creation of truly intelligent chatbots. In this article, learn how Botpress stemmed from frustrations with poorly designed bots that led to a launch of an open-source managed NLU platform. Also, see why the future of chatbot design will shift from an intents-based approach toward knowledge-based models that offer greater adaptability and resiliency.
Developer Accessibility Key to NLP Chatbot Advancement
Chatbots have come a long way over the years, evolving from simple command-response models to the more nuanced NLP conversational models of today.
Looking ahead, NLP shows no sign of slowing. Some projections state the NLP market will reach $127.26 billion by 2028 (up from $20.98 billion in 2021). This is remarkable growth, but it’s also not all that surprising given current consumer demands.
Across virtually all digital experiences, consumers now expect personalization and ease. It’s here where NLP chatbots are poised to truly shine by creating natural humanistic conversations tailored to the end user’s experience. Delivering on that reality is another story altogether. It all comes down to whether NLP achieves widespread accessibility by developers.
Generally, developers have two options when building an NLP chatbot. The first is the in-house route, where developers build their bots on the back of complex software like Rasa. The benefit of going in-house is that it provides a highly customizable solution for experienced Python developers. On the flip side, however, implementation is time-consuming and requires an experienced team of developers, data scientists, and ML experts closely managing and updating their NLU models in order to successfully execute. Further, some options lack a user-friendly visual tool, making the learning curve extremely steep for non-technical users.
Another major strike against this route is that development happens in a closed ecosystem. As a result, the end product often runs the risk of being out-of-date compared to current NLP models when it actually hits the market.
The second development option is through a “buy off the shelf” solution. As the majority of the heavy development lifting is completed, non-technical teams or teams with limited NLU experience will find this option more favorable. Because development time is significantly reduced, this option provides a quicker time-to-market and requires lower overhead for staff resourcing. On the downside, little customization is available with this option making it a less favorable choice for scenarios where complex and dynamic conversation flows are desired.
And then there’s a third option: a place where developers can freely create robust conversational experiences without significant NLP expertise. This is where Botpress sits.
Botpress
Soon after the chatbot boom began to take shape and inspired by my experience building an internal bot, I launched my own chatbot builder. This time, however, I wanted to create an NLP-based solution that would be highly accessible by all developers, regardless of their NLP experience.
Thus, Botpress was born. In the conversation below with Tony Ramos and Jason Gilbert of the Conversation Design Institute Festival, hear the story of how Botpress started entirely unintentionally. We also chatted about the state of NLP chatbot design and future resiliency.
Check out my full chat with the Conversation Design Institute below.
Adaptability Is the Key To Resilient NLP Chatbots
Looking ahead, I believe resilience will be a crucial driver in NLP chatbot development. This resilience is essential given the fluid nature of NLP’s core subject matter: human language.
Language is beautiful; but, it is also nuanced, and at times, sloppy. As such, NLP chatbot build solutions need to be resilient to the nature of language. To achieve that, they must be adaptive.
One way I see the NLP chatbot build space achieving that is through a shift away from the standard intent-based approach. Intents-based models rely heavily upon a consensus from user inputs to function properly. The problem is that humans rarely consistently reach an agreement on the specific questions they’re asking a chatbot. If humans can’t reach an agreement, how can we expect computers to produce a consensus in outputs?
Intents, in my opinion, are limiting to NLP advancement in that they lack long-term adaptability.
One way we’re working to achieve adaptability as a core driver of the Botpress platform is through a shift towards knowledge-based task fields. These tasks enable developers to build bots that only extract and act upon the vital performative words within a sentence. This process also omits all unnecessary information that a user may input (including typos and contextual nuances). What remains is a clean data set that the bot can appropriately interpret and respond to as desired.
Another way we’re achieving adaptability is through the open-source nature of our platform. As an open-source solution, the contributions of Botpress users ensure our product is continuously improved. By doing so, our platform can keep in step with evolutions in NLP technology as they happen.
To learn more about Botpress’ documentation, check out our Developer’s Guide, or head over to our Community Forum to hear what other developers think of our platform.
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