Data-Driven Dialogue Systems for Social Agents

TL;DR

This paper advocates for a data-driven approach to develop social dialogue systems by analyzing large social media corpora and integrating NLP modules to create more personable and nuanced social agents.

Contribution

It introduces a framework combining social media data analysis with NLP modules to enhance social dialogue systems for more human-like interactions.

Findings

Analyzed large social media datasets for conversational insights.

Integrated sentiment, style, and topic modeling into dialogue systems.

Demonstrated potential for more personable social agents.

Abstract

In order to build dialogue systems to tackle the ambitious task of holding social conversations, we argue that we need a data driven approach that includes insight into human conversational chit chat, and which incorporates different natural language processing modules. Our strategy is to analyze and index large corpora of social media data, including Twitter conversations, online debates, dialogues between friends, and blog posts, and then to couple this data retrieval with modules that perform tasks such as sentiment and style analysis, topic modeling, and summarization. We aim for personal assistants that can learn more nuanced human language, and to grow from task-oriented agents to more personable social bots.