18053585. MULTI-INSTANCE, MULTI-ANSWER TRAINING FOR TABLE AND TEXT QUESTION ANSWERING simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

MULTI-INSTANCE, MULTI-ANSWER TRAINING FOR TABLE AND TEXT QUESTION ANSWERING

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Vishwajeet Kumar of Bangalore (IN)

Saneem Ahmed Chemmengath of Bangalore (IN)

Jaydeep Sen of Bangalore (IN)

MULTI-INSTANCE, MULTI-ANSWER TRAINING FOR TABLE AND TEXT QUESTION ANSWERING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18053585 titled 'MULTI-INSTANCE, MULTI-ANSWER TRAINING FOR TABLE AND TEXT QUESTION ANSWERING

Simplified Explanation

The patent application describes techniques for enhanced table and text question answering based on multi-instance, multi-answer training. An answer extractor component analyzes data items associated with a table and passage data items to determine answer scores for candidate answers based on the context of a query. The answer extractor component is trained using denoised single-instance and multiple-instance answer matching data to generate a trained model. A query response component then selects the correct answer data item based on the answer scores, with candidate answers reranked using reweighted scores.

• Answer extractor component analyzes data items associated with a table and passages to determine answer scores for candidate answers.
• Trained using denoised single-instance and multiple-instance answer matching data to generate a trained model.
• Query response component selects correct answer based on scores, reranking candidate answers.

Potential Applications

This technology could be applied in various fields such as information retrieval systems, question-answering systems, and natural language processing applications.

Problems Solved

This technology helps improve the accuracy and efficiency of question-answering systems by enhancing the process of selecting the correct answer from a set of candidates.

Benefits

The benefits of this technology include improved accuracy in answering questions, faster response times, and enhanced user experience in interacting with question-answering systems.

Potential Commercial Applications

Potential commercial applications of this technology include search engines, virtual assistants, customer support chatbots, and educational platforms.

Possible Prior Art

One possible prior art could be the use of machine learning models for question-answering systems, but the specific approach of training based on denoised single-instance and multiple-instance answer matching data may be novel.

How does this technology compare to existing question-answering systems in terms of accuracy and efficiency?

This article does not provide a direct comparison with existing question-answering systems to evaluate the performance improvements offered by this technology.

What are the potential limitations or challenges in implementing this technology in real-world applications?

The article does not address any potential limitations or challenges that may arise in implementing this technology in practical settings, such as scalability issues or data privacy concerns.

Original Abstract Submitted

Techniques for enhanced table and text question answering based on multi-instance, multi-answer training are presented. An answer extractor component can determine answer scores associated with candidate answer data items based on analysis of a set of data, comprising row data items of a table and passage data items associated with the table, and a context of a query of the set of data. The answer extractor component can be trained based on application of denoised single-instance and multiple-instance answer matching data associated with contexts to an answer extractor model to generate a trained answer extractor model of the answer extractor component. A query response component can determine a correct answer data item responsive to the query from the candidate answer data items based on the answer scores associated with the candidate answer data items, wherein the candidate answer data items can be reranked based on reweighted answer scores.