A bilingual chatbot using support vector classifier on an automatic corpus Engine dataset

Brands are shifting to digital services to cater to their customers who have been spending more time online. The technology that exists today enhances customer experience and actualizes customer expectations through virtual service agents or \u27e-service agents\u27 during real-time interactions. Brands in most countries have to deal with bilingual customers as globalization occurs. Business process outsourcing is among the Philippines\u27 top foreign exchange earner aside from overseas workers\u27 remittances. These Philippine companies offer customer service but are mostly left manned-needing constant supervision. As a solution, the researchers present a bilingual retail chatbot that could handle the two official languages of the Philippines, Filipino-based on Tagalog-and English, and their code-switching variant Taglish. The proposed bilingual retail chatbot uses k-fold grid search cross-validation on a dataset constructed by a bilingual automatic corpus engine and a combination of both (1) support vector classifier-for intent identification, and (2) hash set containment-for attribute identification. © 2020 IEEE