A task-oriented dialogue bot using long short-term memory with attention for Thai language

Abstract

A task-oriented dialogue bot helps users achieve a predefined goal within a closed domain. A neural-network based dialogue bot tracks the user intention in each action, which can reach promising performance compared to a hand-crafted baseline [1] and has a more flexible conversational flow. One such end-to-end architecture is the Hybrid Code Networks (HCNs) [2]. It uses the simulated conversation of human-bot in the domain of restaurant booking to train an LSTM to track dialogue states and predict the next bot response. This research proposes a similar architecture to HCNs with the addition of attention to LSTM [3]. The best results are obtained by our model on both original and Thai translated versions of bAbI task 5.