MULTI-MODAL ARABIC NEGOTIATING BOT

Abstract

Negotiation is a fundamental aspect of human interaction, involving a dynamic exchange of communication between two or more parties to reach mutually agreeable outcomes. With recent advancements in chatbots, leveraging artificial intelligence (AI) for negotiation has emerged as an ideal application. Despite significant progress in English negotiation bots using deep learning and reinforcement learning, such advancements are notably absent in other languages, particularly Arabic. Furthermore, while previous research has primarily focused on developing high-performing neural response generation systems for negotiation bots, the integration of multimodality into these automated agents remains unexplored. The incorporation of multimodality is represented in image analysis, and it contributes to a more comprehensive and userfriendly negotiation model. This thesis presents the first Arabic negotiation model, distinguished by incorporating multimodality into negotiation models. The integration of multimodality, particularly through image analysis, provides a more comprehensive and user-centric approach to negotiation. Our primary objective is to develop an Arabic multimodal negotiating bot, a seller agent capable of engaging in negotiations with buyers in the context of item sales. This seller agent is designed to understand the buyer's Arabic utterances and to interpret the negotiation context through images provided by the buyer. To achieve this, we trained a Generative Pre-trained Transformer (GPT-2) model on an Arabic dataset, integrating it with a Convolutional Neural Network (CNN) for image analysis. The model's automatic evaluation yielded a BLEU4 score of 0.21 and a cross-entropy loss of 0.55, metrics that are promising for the first model of its kind in Arabic. Our experiments and analyses reveal both the successes and limitations of the designed multi-modal Arabic negotiating model, offering insights into the inherent challenges and setting directions for future research.