Pavan Kapanipathi, Alexander Gray, and Salim Roukos

In this blog, we describe Neuro-Symbolic Question Answering, a system that uses a semantic parser and a neuro-symbolic reasoner for Knowledge Base Question Answering (KBQA). KBQA has emerged as an important Natural Language Processing task because of its commercial value for real-world applications. KBQA requires a system to answer a natural language question by reasoning over facts in a Knowledge Base (KB). For a KBQA system the following sub-tasks remain challenging: (1) question understanding, particularly for compositional questions involving multiple relationships, (2) lack of end-to-end (text to SPARQL) training data with realistic questions…

Nandana Mihindukulasooriya and Ibrahim Abdelaziz

Knowledge base question answering (KBQA) has emerged as an important Natural Language Processing task for many real-world applications. Relationship Extraction and L­inking (REL) is a crucial sub-task of KBQA that involves identifying the relations in natural language questions and linking them to their equivalent relations in the underlying knowledge base. These relations are then used to construct a query to retrieve the question answers. For example, given the natural language question “Who is starring in Spanish movies produced by Benicio del Toro?”, REL should identify 3 relations; “dbo:producer”, “dbo:starring”, and “dbo:country” when using DBpedia as…

One approach for building a question answering system to answer users’ questions from linked data (or databases/knowledge graphs) is to use a deep learning model with a large application specific training set of question-answer pairs to train an end-to-end system. An alternative neuro-symbolic approach would be to build a system by combining several components each trained on its own generic dataset with very small amounts of application specific qa pairs on the order of a couple hundred or less to adapt to the application domain.

In this neuro-symbolic system, we rely on a semantic parser to parse the input question…

IBM Research addressing Enterprise NLP challenges in 2020

The field of Natural Language Processing (NLP) has made large strides over the last decade. In fact, NLP is so common in today’s AI applications that whether consumers are communicating with a virtual assistant, asking for travel directions or searching for weather predictions, chances are they’re interacting with some form of NLP.

This technology, however, still faces significant hurdles. At its core, NLP attempts to help an AI communicate with humans in natural language. Yet, for an NLP system to master language, it must be able to both generalize and reason over…

Answering users’ questions in an enterprise domain remains a challenging proposition. Businesses are increasingly turning to automated chat assistants to handle technical support and customer support interactions. But, these tools can only successfully troubleshoot questions they were trained on, exposing a growing challenge for enterprise question answering (QA) techniques today.

To address this, IBM Research AI is introducing a new leaderboard called TechQA which uses real world questions from users posted on IBM DeveloperWorks. The goal of TechQA is to foster research on enterprise QA, where learning from a relatively small set of QA pairs is the more realistic condition.

By Salim Roukos, IBM Fellow

Finding information in a company’s vast trove of documents and knowledge bases to answer users’ questions is never as easy as it should be. The answers may very well exist, but they often remain out of reach for a number of reasons.

For starters, unlike the Web, where information is connected through a rich set of links and is often captured redundantly in multiple forms (making it easier to find), enterprise content is usually stored in silos with much less repetition of key information. In addition, users searching enterprise content typically ask intricate questions and…

Salim Roukos

IBM Fellow, working on multilingual NLP using Machine (and Deep) Learning models for language translation, information extraction, and language understanding.

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