TY - JOUR
T1 - A BERT-based Question Answering Architecture for Spanish Language
AU - Ramos, Robert C.Gutierrez
AU - Calderón-Vilca, Hugo D.
AU - Cárdenas-Mariño, Flor C.
N1 - Publisher Copyright:
© MIR Labs, www.mirlabs.net/ijcisim/index.html
PY - 2022
Y1 - 2022
N2 - QA systems have had various approaches to achieve their goal of solving naturally formed questions, recent works use state of the art techniques such as neural networks, QA systems in different languages are increasing, as evidenced, they are advancing at different rates, despite the fact that there are efforts to increase research in this type of systems. In this research we analyze the main aspects of the contributions to Question Answering and present an architecture that is capable of answering questions in Spanish. The initial question is received by the system, which may or may not have a document corpus from which to extract the answer, if it does not have a specified document corpus, the Answer Generation module returns the answer to the initial question. The purpose of the system is to provide answers to factoid questions posed by users through a web and mobile platform. BI-LSTM was used for document retrieval and BERT was used to generate the answers. We tested the architecture with ten thousand questions reaching an accuracy of 0.7856.
AB - QA systems have had various approaches to achieve their goal of solving naturally formed questions, recent works use state of the art techniques such as neural networks, QA systems in different languages are increasing, as evidenced, they are advancing at different rates, despite the fact that there are efforts to increase research in this type of systems. In this research we analyze the main aspects of the contributions to Question Answering and present an architecture that is capable of answering questions in Spanish. The initial question is received by the system, which may or may not have a document corpus from which to extract the answer, if it does not have a specified document corpus, the Answer Generation module returns the answer to the initial question. The purpose of the system is to provide answers to factoid questions posed by users through a web and mobile platform. BI-LSTM was used for document retrieval and BERT was used to generate the answers. We tested the architecture with ten thousand questions reaching an accuracy of 0.7856.
KW - Artificial intelligence
KW - Information retrieval
KW - Learning systems
KW - Machine learning
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85130462176&partnerID=8YFLogxK
M3 - Artículo
AN - SCOPUS:85130462176
SN - 2150-7988
VL - 14
SP - 119
EP - 127
JO - International Journal of Computer Information Systems and Industrial Management Applications
JF - International Journal of Computer Information Systems and Industrial Management Applications
ER -