It is always important to evaluate the quality of your chatbots and conversational agents in order to know the its real health, accuracy and efficiency. Chatbot accuracy can only be increased by constantly evaluating and retraining it with new data that answers your customer's queries. 

Chatbots require large amounts of training data to perform correctly. If you want your chatbot to recognize a specific intent, you need to provide a large number of sentences that express that intent, usually generated by hand. This manual generation is error-prone and can cause erroneous results.

How can we solve it?

With artificially-generated data. Since Dialogflow is one of the most popular chatbot-building platforms, we chose to perform our tests using it.

Chatbots can improve customer experience in contact centers by:

• Reducing customer wait time
• Achieving a higher customer satisfaction
• Cutting down contact center expenses and increasing productivity
• Getting to know your customer better
• Using human agents only when it is necessary

Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.

The following practices will help you design a great conversation between your chatbot and your client:

• Transparency
• Avoid using an excess of predetermined links and buttons
• Use an NLP middleware approach
• Include politeness and small talk
• Tolerate typos and slightly ambiguous formulations
• Always include domain specific terminology
• Define your bot's tone
• Keep the number of possibilities limited
• Let the user know when the bot is "thinking" or processing the query

Reducing complicated, confusing processes down to a natural conversation is potentially a huge business opportunity for anyone willing to jump headfirst and create a great user experience. Chatbots are only as smart as the words you feed them. If a bot is too rudimentary, people will lose trust in the company and will feel ignored and unappreciated. UX problems appear when the user deviates from the designed linear flow.

One of the flaws of usual training data generation is that, when you ask somebody to manually create training data for you, they will make an effort to write these sentences correctly, following the spelling and punctuation norms of your language. Even if some errors appear, they will be minimal, because they are trying to do things right —this is, to provide “orthographically right” sentences.

All Machine Learning (ML) engines that work with text can benefit from a solid linguistic background. If they are working in a multilingual environment, the need of a good lexicon (with forms, lemmas and attributes) is overwhelming. Even so, basic features such as Word Embeddings hugely improve when enriched with linguistic knowledge, and if this is not usually applied, is because of a lack of linguists working for ML companies.

Bitext’s is industrializing training data production for any voice-controlled device, chatbot or IVR using artificial training data to accelerate customer support automation. At Bitext we solve data scarcity and legal risks with Multilingual Synthetic Training Data to enhance Conversational AI and to derive insights from text-based and unstructured data such as contact center interactions, chat-bot and live chat transcripts, product reviews, open-ended survey responses and email. We can natively analyze text in up to 80 languages.