Natural Language Interface for Covid-19 Amharic Database Using LSTM Encoder Decoder Architecture with Attention

Abstract

The COVID-19 outbreak is still a challenge in most places because of lack of up-to-date information, primarily, to the people in the world who speak and use underrepresented local languages. Ethiopia is one example of a country where several in-digenous languages are under-represented and under-resourced. Thus, building an interactive interface that responds to users' query using their local language with organized information plays a significant role. In this study, attention-augmented Encoder-Decoder Long Short Term Memory(LSTM) network model has proposed to provide adequate information about the pandemic to the people of Ethiopia by their local language, Amharic. The model converts Amharic COVID-19 related questions into the corresponding structured query language (SQL). The model retrieves information from the Amharic COVID-19 database that has developed for this study. The database contains frequently referenced COVID-19 attributes such as symptoms, prevention, transmission and frequently asked questions. In addition, a parallel Amharic Question-SQL query dataset has been prepared to evaluate the model. The LSTM Network with augmented attention mechanism has shown a clear significant result. In this study, a user interactive interface has also developed. The interface uses the proposed model and provides information about the pandemic to the people with questions in Amharic.