PRIORITY CLAIM

This application claims priority to India Provisional Application No. 202011007948, filed Feb. 25, 2020, which is hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates to information extraction from documents and more specifically to systems and methods for extracting information of interest from unfamiliar documents using zero-shot learning.

BACKGROUND

Information extraction involves automatically extracting structured information from either structured or unstructured documents. In some cases, the unstructured documents are human language texts where natural language processing (NLP) is applied to extract the structured information. NLP includes a broad set of computing algorithms for analyzing and representing human language. NLP-based systems have benefited from Deep Learning, enabling applications such as generating machine translations of documents and dialogue generation for automated chat systems or various smart speakers.

Even with the progress in NLP, state of the art approaches to NLP still rely upon traditional machine learning paradigms due to the complex nature of human language. Traditional machine-learning paradigms have certain drawbacks, including, e.g., being computationally costly, requiring a wealth of available training data, requiring a fairly high skill of system engineers, having high energy costs, etc. In addition to these costs and requirements, traditional machine learning paradigms are domain-limited when applied to information (and/or document) extraction. That is, to perform information extraction with an acceptable level of accuracy, traditional machine learning paradigms require training a machine learning model with a text corpus from a specific domain, such that the machine learning model can capture verbiage or tendencies in the specific domain which may not be transferable to other domains. Training can be expensive and prohibitive, as such, the present disclosure provides systems and methods for information extraction that bypasses the extensive training of traditional machine learning paradigms.

SUMMARY

An embodiment of the present disclosure provides a system for performing document extraction. The system includes a non-transitory computer-readable medium storing computer-executable instructions thereon such that when the instructions are executed, the system is configured to: (a) receive a first document; (b) extract the first document into document elements, the document elements including pages, lines, paragraphs, or any combination thereof; (c) determine a first set of fields of interest for the first document, wherein the first set of fields of interest are determined via a type of the first document or via a first set of queries for probing the first document; (d) determine, from a plurality of closed domain question answering (CDQA) models, a first set of CDQA models that provides answers to each field of interest included in the first set of fields of interest; and (e) provide answers to the first set of fields of interest to the client device.

The foregoing and additional aspects and implementations of the present disclosure will be apparent to those of ordinary skill in the art in view of the detailed description of various embodiments and/or implementations, which is made with reference to the drawings, a brief description of which is provided next.

