Why Today’s Multilingual AI Chatbot Technology Fails

Is your Chatbot truly “AI”?

Natural Language technologists have long been on the mission of making conversational agents that can emulate the nimble ways of conversion of that of a human, but today’s technology is only mere steps closer to realizing that mission.

There is a gap between what Conversational AI, in its instance of AI Chatbots claims to be, and how much of that claim can be supported by the technology in the market?

In this article, we take a look beyond what forms the fulcrum of fluid Dialog Management, robust multilingualism, and viable customer experience. We also look at how deploying “AI” Chatbots with limited technology can hurt the business value.

Unintuitive Conversations with Chatbots Hurt Business Value

Chatbots with intuitive and limited flows can leave businesses with uninspired customers.

Building an AI Chatbot with today’s technology can result in dwindling business value and experience for your end-users. Many CXOs understand this and are reluctant in adopting the technology.

A glimpse under the hood of today’s AI-driven chatbots reveal mundanely scripted agents, veneered with cutting-edge Machine Learning algorithms.

Dialogue management in such Natural Language Understanding Engines (NLU) is therefore limited to rules that can hardly capture the nuances of a normal conversation, that can generally take any direction, at any point.

Business Value is Lost in Translation with Multilingual AI Chatbots

Conversational AI today has been built around English, meaning, it is essentially broken for non-English inputs.

In an attempt to deliver multilingual agents, AI and NLP practitioners have resorted to using Machine Translation (MT), which leads to misrepresentation of inputs and loss of information.

Often, this involves the risk of triggering incorrect conversational flows, misunderstood customer-query, and ultimately, erroneous actions performed by the bot.

Business value is ultimately lost in translation, and a brand is dealt with irreparable damage to its image.

Negative Emotive Experiences Lead to Churn

Customers that are offered a negative emotive experience with brands do not think twice about switching brands. They wish to be treated as more than just an account to their business, and demand for a personalized experience.

In AI chatbots, the true mark of intelligence isn’t language understanding alone, but also an emotional quotient.

Customers interacting with bots today experience a synthetic conversational experience. This can lead to customer ire. It holds especially true when the bot faces an irate customer. The bot can be immediately rebuffed by him, and that could ultimately mean that your business just lost another customer.

How can language technology evolve to not cause loss to a brand’s reputation?

Eliminating the Need for Translation

Native Language Understanding Engines (NLU) today depend on Machine Translation (MT) to understand non-English languages. This causes loss of information, as languages have varying semantics, and translation from English does not capture such nuances.

How does technology evolve to build conversational agents that don’t result in misfired conversational flows, translation losses, and not cause harm to a brand’s reputation?

It is essential to build these multilingual conversational agents on an NLU that is built ground-up from a native language NLP stack, modeled from different languages rather than translating from English.

Conversation-Analysis and Adapting to Emotive Signals

Personalization is a buzzword in the industry. Customers demand it and feel that they’re valued by brands that offer it. At the end of the day, customers have a better opinion of a brand that treats them as more than just accounts or phone numbers.

But, in an AI Bot, personalization can be more than just recommendations. Customers will value their engagement with a bot that understands their sentiments at various levels, to create empathetic experiences.

Natural Language Understanding Engines (NLU) that can capture multiple substantive cognitive signals from the conversation, can give bots the ability to adapt their responses to these signals. Whether a customer is angry, sad or happy, the bot should be able to handle it effectively. This will also give the brand a better view of their customer sentiment towards the brand.

Further, analyzing the conversations against several key KPIs will help in making informed decisions and provisioning the changes in the AI Chatbot accordingly, to unlock the true potential of business value.

Key takeaways :

• CXOs today must be cognizant of today’s Conversational AI technology, and its pros and cons.
• It is essential to understand the abilities and limitations of language technology to carefully assess solutions that don’t negatively impact the business bottom-line and harm the brand’s reputation.
• For offering multilingual services through AI Chatbots, and before deploying a full-fledged customer-facing Conversational UI, decision-makers must understand the robustness of its language understanding engine.
• Enterprises that deploy AI chatbots must have a canonical mechanism to analyze customers emotionally and be able to provision the bot to respond to their emotions, to offer positive emotive experiences.