US 12,112,135 B2
Question answering information completion using machine reading comprehension-based process
Zhong Fang Yuan, Xian (CN); Tong Liu, Xian (CN); Chen Gao, Xian (CN); and Xiang Yu Yang, Xi'an (CN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Sep. 29, 2021, as Appl. No. 17/449,303.
Prior Publication US 2023/0095180 A1, Mar. 30, 2023
Int. Cl. G06F 40/30 (2020.01); G06F 18/214 (2023.01); G06N 5/04 (2023.01)
CPC G06F 40/30 (2020.01) [G06F 18/2148 (2023.01); G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer system comprising:
a central processing unit (CPU);
a memory coupled to the CPU; and
one or more computer readable storage media coupled to the CPU, the one or more computer readable storage media collectively containing instructions that are executed by the CPU via the memory to cause the processor to implement a question answering system process, comprising:
constructing, by the processor, a training set to detect missing information of a question received by the processor, the question semantically related to a source of information in a document library;
identifying a plurality of components of the question, the components including information terms of the question;
generating a plurality of masked questions from the question, including, for each of the masked questions, masking one component of the plurality of components of the question at a time until each of the plurality of components is masked to generate the masked question;
applying a reading comprehension algorithm to the each masked question to generate new training data;
generating the new training data from a combination of the source of information, the masked component, the question, and an answer assessment generated from a comparison of the source of information and the component of the question that is masked;
receiving, by a reinforcement learning (RL) system, the new training data to predict the missing information of the question;
training, by the processor, a natural language generation model using the missing information, including, in response to the generating the answer assessment, selecting words about which another question is generated for clarifying the question;
executing, by the processor, the natural language generation model to generate the other question clarifying the question using the selected words;
combining, by the processor, a response to the other question and the question to generate an input to a language processor;
generating, by the language processor, a new question;
applying the new question to a document library; and
generating a final answer.