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Deploy Domain-Specific NLP Today to Be Competitive and to Stay on Mission

White conceptual keyboard - NLP (blue key)

According to Gartner, “innovations in NLP, evolutions of conversational UI, and the proliferation of virtual assistants will transform business and social interactions in the next two years.”1 Gartner Source: Emerging Technologies: Research Roundup for NLP and Conversational UI And by 2025, 50% of knowledge workers will use a virtual assistant (VA) on a daily basis and 40% of enterprise applications will have embedded conversational AI.

However, current language and NLP solutions have limitations and are suboptimal, leaving business leaders always looking for ways to improve their enterprise NLP solutions to gain competitive advantage, become cost effective, and enable automation in the enterprise.

Limitations With Current AI Language Solutions

Current NLP solutions follow a traditional approach for their domain-specific (e.g., legal, financial, medical, cybersecurity) fine-tuning tasks. They usually are initialized from “off the shelf” pre-trained NLP models and then are fine-tuned on domain-specific datasets before deployment to serve massive online inference requests from application users.

This traditional approach does not capture domain-specific knowledge during language modeling, and can lead to suboptimal results on any domain-specific fine-tuning task. In other words, pre-training an AI language model with Wikipedia first and then fine-tuning it with your own industry domain-specific context, gives only suboptimal results in your industry-specific chatbots, question answering systems, or sentiment analysis applications.

One of the main reasons behind the lack of widespread adoption of domain-specific training are compute, due to the enormous amount of time required, and the human capital investments required to implement this specialized model training.

Accuracy with Minimal Effort (The Easy Button)

SambaNova Systems, a “full-stack” hardware-software platform company, provides simple “one-click” solutions for both the hardware and software challenges involved in building these pipelines. With SambaNova, application developers can maximize accuracy for domain-specific NLP solutions with minimal effort.

Results include the following:

  • More accuracy and better performance from your machine translation solutions
  • Enriched communications in your organization, you can reach broader audiences, understand regulatory documents and emails in a quick and cost-efficient way
  • Improved monitoring of your social media feeds and visibility into mentions of your brand
  • Better KPIs from your question answering systems, chatbots, virtual assistants, or your sentiment analysis implementation
  • Enhanced text extraction and better text summaries
  • Improved security posture by better parsing of cyber security logs

Learn More

By enabling domain-specific pretraining, SambaNova demonstrates that NLP solutions can offer superior performance and accuracy compared to the standard “off the shelf” industry standards. To help your application and machine learning engineers increase the accuracy of domain-specific NLP solutions in less time than their current approach, please share this white paper with them. To talk with a SambaNova specialist about your particular needs, click here.

Photo of Saniye Alaybeyi
Saniye Alaybeyi

Saniye Alaybeyi leads Product Marketing at SambaNova Systems.

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