Can conversational AI skip NLP training? Yellow AI has a plan

One of the biggest installation challenges facing artificial intelligence (AI) teams is manually training agents. Current guided methods are time consuming and costly, requiring manually labeled training data for all classes. In a study by Dimensional research and AIegion96% of respondents say they have encountered training-related issues, such as data quality, labeling needed to train the model, and building trust in the model.

As the domain of natural language processing (NLP) grows steadily through advances in deep neural networks and large training data sets, this issue has become increasingly important for a range of language-based use cases. To address this, the AI ​​platform for conversations 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 that companies do not have to waste time on continuous training of the NLP model. The tool is based on zero-shot learning (ZSL), which means that companies no longer have to go through the time-consuming process of manually labeling data to train the AI ​​bot. Instead, dynamic AI agents can learn on the fly, set up conversational AI flows in minutes, while reducing training data, costs, and effort.

“Zero-shot learning provides a way around this problem by allowing the model to learn from the intent name,” said Raghu Ravinutala, CEO and co-founder of Yellow AI. “This means the model can learn without having to train in each new domain.”

In addition, the zero-shot model can also reduce the need for data collection and annotation to increase accuracy, he said.

Conversational AI Training Barriers

Conversational AI platforms require extensive training to effectively conduct human-like conversations. Unless utterances are continuously added and updated, the chatbot model doesn’t understand the user’s intent, so it can’t provide the correct answer. In addition, many use cases require the process to be maintained, requiring manual training of NLP with hundreds to thousands of different data points.

When using supervised learning methods to add utterances (input from a chatbot user), it is critical to constantly monitor how users type utterances, step-by-step and iteratively labeling the unidentified utterances. Once tagged, the missing utterances must be reintroduced into the training. During the process, several searches may go unidentified.

Another important challenge is how to add utterances. Even considering all the ways in which user input is logged, it is still questionable how much the chatbot will be able to detect.

To this end, Yellow AI’s DynamicNLP platform is designed to improve the accuracy of visible and invisible intentions in utterances. Removing manual labels also helps eliminate errors, resulting in stronger, more robust NLP with better intent coverage for all types of conversations.

According to Yellow AI, the flexibility of DynamicNLP’s model enables enterprises to successfully maximize efficiency and effectiveness in a wider range of use cases, such as customer support, customer engagement, conversational commerce, HR, and ITSM automation.

“Our platform comes with a pre-trained model with unsupervised learning that allows companies 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 humane and scalable across industries with broader use cases.

“The platform has also been exposed to a lot of domain-related expressions,” he said. “This means that the next generated sentence embeddings are much stronger, with an accuracy of more than 97%.”

Future trends and challenges for conversational AI

Ravintula said the use of pre-trained models to improve the AI ​​development of conversations will undoubtedly increase, including various modalities, including text, speech, video and images.

“Companies across industries would take even less effort to fine-tune and create their unique use cases because they would have access to larger pre-trained models that would deliver a better customer and employee experience,” he said.

One of the current challenges, he pointed out, is to make models more context aware, as language is ambiguous by nature.

“Models that can understand audio input consisting of multiple speakers, background noise, accent, tone, etc., require a different approach to effectively deliver human-like natural conversations with users,” he said.