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One of the biggest setup challenges artificial intelligence (AI) teams face is training agents manually. Current supervised methods are time-consuming and costly, requiring manually labeled training data for all classes. In a survey by Dimensional Research and AIegion, 96% of respondents say they have encountered training-related issues such as data quality, labeling required to train the model and building model confidence.
As the domain of natural language processing (NLP) grows steadily through advancements in deep neural networks and large training datasets, this issue has moved front and center for a range of language-based use cases. To address it, conversational AI platform Yellow AI recently announced the release of DynamicNLP, a solution designed to eliminate the need for NLP model training.
DynamicNLP is a pre-trained NLP model, which offers the advantage of companies not having to waste time on training the NLP model continuously. The tool is built on zero-shot learning (ZSL), which eradicates the need for enterprises to go through the time-consuming process of manually labeling data to train the AI bot. Instead, this allows dynamic AI agents to learn on the fly, setting up conversational AI flows in minutes while reducing training data, costs and efforts.
“Zero-shot learning offers a way to circumvent this issue by allowing the model to learn from the intent name,” said Raghu Ravinutala, CEO and cofounder of Yellow AI. “This means that the model can learn without needing to be trained on each new domain.”
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In addition, the zero-shot model can also mitigate the need for collecting and annotating data to increase accuracy, he said.
Conversational AI training barriers
Conversational AI platforms require extensive training to effectively provide human-like conversations. Unless utterances are constantly added and updated, the chatbot model fails to understand user intent, so it cannot offer the right response. In addition, the process must be maintained for many use cases, which requires manually training NLP with hundreds to thousands of different data points.
When using supervised learning methods to add utterances (a chatbot user’s input), it’s crucial to constantly monitor how users type utterances, incrementally and iteratively labeling the ones that didn’t get identified. Once labeled, the missing utterances must be reintroduced into training. Several queries may go unidentified during the process.
Another significant challenge is how utterances can be added. Even if all the ways in which user input is registered are considered, there is still the question of how many the chatbot will be able to detect.
To that end, Yellow AI’s DynamicNLP platform has been designed to improve the accuracy of seen and unseen intents in utterances. Removing manual labeling also aids in eliminating errors, resulting in a stronger, more robust NLP with better intent coverage for all types of conversations.
According to Yellow AI, the model agility of DynamicNLP enables enterprises to successfully maximize efficiency and effectiveness across a broader range of use cases, such as customer support, customer engagement, conversational commerce, HR and ITSM automation.
“Our platform comes with a pretrained model with unsupervised learning that allows businesses to bypass the tedious, complex and error-prone process of model training,” said Ravinutala.
The pre-trained model is built using billions of anonymized conversations, which Ravinutala claimed helps reduce unidentified utterances by up to 60%, making the AI agents more human-like and scalable across industries with wider use cases.
“The platform has also been exposed to a lot of domain-related utterances,” he said. “This means the subsequent sentence embeddings generated are much stronger, with 97%+ intent accuracy.”
Future trends and challenges for conversational AI
Ravintula said the use of pre-trained models to enhance conversational AI development will undoubtedly increase, encompassing different modalities including text, voice, video and images.
“Enterprises across industries would require even lesser efforts to tune and create their unique use cases since they would have access to larger pre-trained models that would deliver an elevated customer and employee experience,” he said.
One current challenge, he pointed out, is to make models more context-aware since language, by its very nature, is ambiguous.
“Models being able to understand audio inputs that comprise multiple speakers, background noise, accent, tone, etc., would require a different approach to effectively deliver human-like natural conversations with users,” he said.