International business machines corporation (20240160634). MULTI-INSTANCE, MULTI-ANSWER TRAINING FOR TABLE AND TEXT QUESTION ANSWERING simplified abstract

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 20240160634 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 a set of data, including row data items of a table, passage data items associated with the table, and the context of a query, to determine answer scores for candidate answer data items. The answer extractor component is trained using denoised single-instance and multiple-instance answer matching data to generate a trained answer extractor model. A query response component then determines the correct answer data item responsive to the query from the candidate answer data items based on the answer scores, with the candidate answer data items reranked based on reweighted answer scores.

• An answer extractor component analyzes data to determine answer scores for candidate answer data items.
• The answer extractor component is trained using denoised single-instance and multiple-instance answer matching data.
• A query response component determines the correct answer data item based on the answer scores of candidate answer data items.

Potential Applications

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

Problems Solved

This technology helps improve the accuracy and efficiency of question answering systems by enhancing the process of selecting the correct answer from candidate answer data items.

Benefits

The benefits of this technology include improved performance of question answering systems, better user experience, and increased productivity in information retrieval tasks.

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 in this field is the use of machine learning algorithms for question answering systems, but the specific approach of training an answer extractor component based on multi-instance, multi-answer data may be novel.

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

This technology aims to improve the accuracy and efficiency of question answering systems by training an answer extractor component based on multi-instance, multi-answer data. By reranking candidate answer data items, the system can provide more accurate responses to user queries.

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

One potential limitation of this technology could be the computational resources required to train and deploy the answer extractor component in large-scale question answering systems. Additionally, the performance of the system may vary depending on the complexity and diversity of the input data.

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.