December 9, 2020 By Brian Loveys 3 min read

At IBM, we’ve put a focus on developing and expanding enterprise natural language processing (NLP) capabilities designed to help your businesses unearth insights, answer questions and make more informed decisions – even with a small data set or lack of expertise.

While human language is simple enough for a child to grasp, it is incredibly complex for even the most advanced machines – the most challenging part of teaching AI to understand human intent is that it requires massive amounts of data, lots of time, and expertise.

When you ask a question, what are you really trying to say? What goal are you trying to achieve? What information are you really trying to access? Human language is full of nuance, resulting in many ways to express a particular intent. This can be problematic for most AI – like chatbots – which stumble when confronted with the complexity of syntax and latch onto specific words rather than the broader context.

To help enterprises address this challenge, IBM launched a new and improved natural language understanding (NLU) model in IBM watsonx Assistant for intent classification. The new intent detection algorithm is more accurate versus compared commercial solutions in benchmark testing. (1)

Bringing continued NLP advancements from IBM Research to IBM Watson

In addition, we are introducing new NLP advancements within IBM watsonx Assistant and Watson Discovery, now available in beta. Pioneered by IBM Research, the new capabilities are designed to improve the automation of AI and provide a higher degree of precision in NLP.

Reading Comprehension is a feature that returns a specific fact or short answer contained within a long passage. Today, Watson Discovery identifies the best “passages” that correspond with queries. Reading Comprehension retrieves a large number of candidate paragraphs from the set of enterprise documents, searches for an answer to the question at hand and returns the corresponding answers. Reading Comprehension applies contextual understanding to understand queries and leverages massive language models to extract specific answers from the document at hand – and then the user receives a confidence score that indicate how confident the system is in each answer.

This capability is ideal for organizations in the financial industry. For example, if you are trying to make a lending decision, you may need to identify precise facts in complex documents that you would normally be reading and reviewing manually. Previously, Watson Discovery would return suggested paragraphs. With Reading Comprehension, a user will be provided with the precise answer (i.e. “What is the term of this current loan?” “2.9%”), saving them the time of having to manually search through large portfolios of documents. This feature is now available in beta to select Watson Discovery users.

FAQ Extraction, currently available in beta, is a novel answer retrieval technique that crawls web pages to detect FAQs and question-answer pairs, using this content to provide concise, up-to-minute answers through watsonx Assistant.

FAQ Extraction is designed to work in watsonx Assistant’s Search Skill, which looks for answers to end-users’ questions in documentation. This functionality makes it more likely that end-users find the answers they need when interacting with AI-powered virtual agents.

For example, businesses may struggle to keep up with ever-changing public guidance around permitted returns to the workplace or re-opening brick and mortar stores. It would require enormous resources to keep AI-powered customer care solutions up to date without a mechanism like FAQ Extraction. Instead, watsonx Assistant can keep up with the latest information simply by knowing the URL of authoritative FAQ content.

Finally, Watson NLP solutions now support 10 additional languages. IBM Watson Discovery now supports Bosnian, Croatian, Danish, Finnish, Hebrew, Hindi, Norwegian (Bokmål), Norwegian (Nynorsk), Serbian, and Swedish, while Watson Natural Language Understanding (NLU) now provides support for Danish, Norwegian Bokmal, Norwegian Nynorsk, Finnish, Czech, Hebrew, Polish, and Slovak (for Keywords).

These advancements build on a pipeline of NLP innovation from IBM Research. Earlier this year, we announced that we were taking some of the core NLP technologies powering IBM Research’s Project Debater – including advanced sentiment analysis (idiom understanding), summarization, topic clustering and key point analysis — and commercializing them within IBM’s NLP products, like Watson Discovery.

These innovations can help businesses to further understand and derive real value from their business data, so they can make more informed decisions, and provide customers and employees with more efficient insights.

Statements regarding IBM’s future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

_____________________________________________________________________________

(1) In November 2020, Jio Haptik Technologies, a conversational AI software company, published a technical paper in which they compared the performance of their product against similar offerings from Google, Microsoft, and RASA. The performance of the other commercial solutions aside from IBM watsonx Assistant was taken from the Arora et al. (2020) benchmarking study. IBM ran the same performance tests on IBM watsonx Assistant as were reported by Arora et al. for purposes of this analysis. IBM’s full results are available in this technical paper.

Was this article helpful?
YesNo

More from Artificial intelligence

Empower developers to focus on innovation with IBM watsonx

3 min read - In the realm of software development, efficiency and innovation are of paramount importance. As businesses strive to deliver cutting-edge solutions at an unprecedented pace, generative AI is poised to transform every stage of the software development lifecycle (SDLC). A McKinsey study shows that software developers can complete coding tasks up to twice as fast with generative AI. From use case creation to test script generation, generative AI offers a streamlined approach that accelerates development, while maintaining quality. This ground-breaking technology…

What you need to know about the CCPA draft rules on AI and automated decision-making technology

9 min read - In November 2023, the California Privacy Protection Agency (CPPA) released a set of draft regulations on the use of artificial intelligence (AI) and automated decision-making technology (ADMT). The proposed rules are still in development, but organizations may want to pay close attention to their evolution. Because the state is home to many of the world's biggest technology companies, any AI regulations that California adopts could have an impact far beyond its borders.  Furthermore, a California appeals court recently ruled that…

In preview now: IBM watsonx BI Assistant is your AI-powered business analyst and advisor

3 min read - The business intelligence (BI) software market is projected to surge to USD 27.9 billion by 2027, yet only 30% of employees use these tools for decision-making. This gap between investment and usage highlights a significant missed opportunity. The primary hurdle in adopting BI tools is their complexity. Traditional BI tools, while powerful, are often too complex and slow for effective decision-making. Business decision-makers need insights tailored to their specific business contexts, not complex dashboards that are difficult to navigate. Organizations…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters