Data-Driven Dialogue Systems for Social Agents
This work addresses the problem of creating more human-like social agents for users, but it is incremental as it builds on existing data-driven and modular NLP approaches.
The paper tackles building dialogue systems for social conversations by analyzing large corpora of social media data and integrating NLP modules like sentiment analysis and topic modeling, aiming to develop more personable social bots from task-oriented agents.
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.