Designing Automated Conversational Tools for Intent Learning, Curated Information Presenting, Fake News Alerting - Application in COVID-19, and Providing Legal Advice

Context & Background

Emergencies like pandemic outbreaks require rapid distribution of factual, curated information. Simultaneously, access to legal aid is highly sought after during crises. Conversational tools using AI and natural language processing can act as highly accessible portals to solve these public issues.

Problems to be Addressed

Public platforms are flooded with misinformation and fake news, causing public panic. Building conversational agents that can detect user intent accurately while cross-referencing information against verified databases is computationally challenging.

Aims and Objectives

1. Build automated chatbots for intent classification under noisy inputs.
2. Design fact-checking pipelines to detect and alert users about fake news.
3. Implement legal guidance query-answering frameworks.

Methodology

The project builds NLP transformer models trained on domain-specific COVID-19 and legal text libraries. The conversational middleware implements graph-based intent maps and uses retrieval-augmented generation (RAG) to serve factual documents only.

Expected Outcomes

Automated dialogue agents, fake-news detection algorithms, and interactive question-answering systems for legal and medical aid.