Intent Detection and Slots Prompt in a Closed-Domain Chatbot

TL;DR

This paper presents a multi-staged approach for intent detection and slot filling in a career-related chatbot, improving accuracy by iterative decision-making and fuzzy entity matching, achieving state-of-the-art results.

Contribution

Introduces a staged methodology where intent detection and slot filling inform each other, enhancing performance in a closed-domain chatbot.

Findings

Achieved F1-score of 77.63% for intent classification.

Achieved F1-score of 82.24% for slot-tagging.

State-of-the-art performance on the proposed dataset.

Abstract

In this paper, we introduce a methodology for predicting intent and slots of a query for a chatbot that answers career-related queries. We take a multi-staged approach where both the processes (intent-classification and slot-tagging) inform each other's decision-making in different stages. The model breaks down the problem into stages, solving one problem at a time and passing on relevant results of the current stage to the next, thereby reducing search space for subsequent stages, and eventually making classification and tagging more viable after each stage. We also observe that relaxing rules for a fuzzy entity-matching in slot-tagging after each stage (by maintaining a separate Named Entity Tagger per stage) helps us improve performance, although at a slight cost of false-positives. Our model has achieved state-of-the-art performance with F1-score of 77.63% for intent-classification and 82.24% for slot-tagging on our dataset that we would publicly release along with the paper.