Laboratory for the Study of Applied Language Technology and Society

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Identifying the Language of Opposition in Online Interactions

Current members:

  • Faculty: Mark Aakus, Smaranda Muresan,Nina Wacholder;
  • PhD Students: Debanjan Ghosh

Past members: Soon-Yeon Hwang, Alexander Pichugin

Opposition is a ubiquitous element of human behavior: in any extended interaction between two or more people, at least some expression of opposition is inevitable. The goals of this project are i) to advance our understanding of how expression of opposition shapes the ebb and flow of online interactions and ii) to develop computational models capable of identifying and characterizing the expression of opposition in multi-party interactions. The robust natural language processing models developed to identify opposition will be useful for studying other social phenomena such as collaboration and decision making. This research will advance our understanding of how and when opposition is expressed and of the function it serves in large-scale online communities across a variety of collaborative activities.

Teaching Computers to Follow Verbal Instructions

NSF RI Medium Collaborative Research project (IIS-1065195) (Michael Littman PI)

Current members:

  • Faculty: Marie desJardins (UMBC), Michael Littman (Rutgers), Smaranda Muresan (Rutgers)
  • PhD Students: Monica Babes-Vroman(Rutgers), Ruoyuan Gao (Rutgers), James MacGlashan(UMBC), Kevin Winner (UMBC)
  • Master Students: Richard Adjogah (UMBC)

The goal of this research is to develop techniques that will permit a computer or robot to learn from examples to carry out multipart tasks specified in natural language on behalf of a user. It will study each of these components in isolation, but a significant focus will be on integrating them into a coherent system. The project will also leverage this technology to provide an entry point to educate non- or pre-computer science students about the capabilities and utility of computers as tools.

Our approach uses three main subcomponents, each of which requires innovative research to solve its portion of the overall problem. In addition, the integrated architecture is a novel contribution of this work. The three components are (1) recognizing intention from observed behavior using extensions of inverse reinforcement learning, (2) translating instructions to task specifications using novel techniques in the area of natural language processing, and (3) creating generalized task specifications to match user intentions using probabilistic methods for creating and managing abstractions.

The goal of the work is develop technology for an improved ability for human users to interact with intelligent agents, the incorporation of novel AI research insights and activities into education and outreach activities, and the development of resources for the AI educator community. In addition to permitting intelligent agents to be developed and trained in the future for a broad range of complex application domains, the interactive agents that we will develop will be used for outreach and student learning.