Posts Tagged ‘text cbr’

28 Nov

Conversational AI: The Science behind the Alexa Prize

Posted by cognitivecomputing in Agents, Language, Learning, Talks. Tagged: , , , , , , , , , , , , .

Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents, known as “socialbots”, to converse coherently and engagingly with humans on popular topics such as Sports, Politics, Entertainment, Fashion and Technology for 20 minutes.

The Alexa Prize offered the academic community a unique opportunity to perform research with a live system used by millions of users. The competition provided university teams with real user conversational data at scale, along with the user-provided ratings and feedback augmented with annotations by the Alexa team. This enabled teams to effectively iterate and make improvements throughout the competition while being evaluated in real-time through live user interactions.

To build their socialbots, university teams combined state-of-the-art techniques with novel strategies in the areas of Natural Language Understanding, Context Modeling, Dialog Management, Response Generation, and Knowledge Acquisition. To support the teams’ efforts, the Alexa Prize team made significant scientific and engineering investments to build and improve Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice User Experience, and tools for traffic management and scalability.

This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational AI.

Conversational AI: The Science behind the Alexa Prize

by Ashwin Ram, Rohit Prasad, Chandra Khatri, Anu Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, Art Pettigrue

Proceedings of the 2017 Alexa Prize
Invited talk at NIPS-2017 Workshop on Conversational AI
Invited talk at re:Invent 2017 (with Spyros Matsoukas)

READ THE PAPER:

arxiv.org/abs/1801.03604

WATCH THE TALK:

youtu.be/pn5QJQZjGpM

			

		


	
		

			

				15
				Nov
			

			
Socio-Semantic Conversational Information Access

			

			Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0.			Tagged: , , , , , , .			

			

				
The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is ‘aware’ of users’ preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users’ verbal intentions in conversations while making recommendation decision.


One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain.


Cobot leverages these interactions to maintain users’ episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user’s interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation.


The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.


Read the dissertation:


Socio-Semantic Conversational Information Access


by Saurav Sahay


PhD dissertation, College of Computing, Georgia Institute of Technology, November 2011.


smartech.gatech.edu/handle/1853/42855

			

		


	
		

			

				27
				May
			

			
Open Social Learning Communities

			

			Posted by cognitivecomputing in Agents, Education, Health & Wellness, Talks, Web / Web 2.0.			Tagged: , , , , , , .			

			

				
With the advent of open education resources, social networking technologies and new pedagogies for online and blended learning, we are in the early stages of a significant disruption in current models of education. The disruption is fueled by a staggering growth in demand. It is estimated that there will be 100 million students qualified to enter universities over the next decade. To educate them, a major university would need to be created every week.


Universities have responded to this need with Open Education Resources—thousands of free, high quality courses, developed by hundreds of faculty, used by millions worldwide. Unfortunately, online courseware does not offer a supporting learning experience or the engagement needed to keep students motivated. Students read less when using e-textbooks; video lectures are boring; and retention and course completion rates are low.


Therein lies the core problem: How to engage a generation of learners who live on the Internet yet tune out of school, who seek interaction on Facebook yet find none on iTunes U, who need community yet are only offered content. We propose a new approach to this problem: open social learning communities, anchored with open content, providing an interactive online study group experience akin to sitting with study buddies on a world-wide campus quad.


This solution is enabled by state-of-the-art web technologies: really real-time collaboration technologies for a highly interactive experience; intelligent recommender systems to help learners connect with relevant content and other learners; mining and analytics to assess learner outcomes; and reputation techniques to establish social capital.  We will discuss these technologies and how they can be combined to address the problem of education in a manner that is highly scalable yet interactive and engaging.


This approach can be used for other types of learning communities. We will show an application to healthcare information access to help consumers learn about their healthcare questions and needs.


Keynote talk at SIPA Conference: Entrepreneurship—Idea Wave 3.0, Mountain View, CA, November 12, 2011.


 


Keynote talk at the International Conference on Web Intelligence, Mining and Semantics (WIMS-11), Sogndal, Norway, May 27, 2011.


 


View the talk:


videolectures.net/wims2011_ram_learning


Read the paper:


www.cc.gatech.edu/faculty/ashwin/papers/er-11-04.pdf


View the slides:




 


 


 

			

		


	
		

			

				10
				Jul
			

			
Conversational Framework for Web Search and Recommendations

			

			Posted by cognitivecomputing in Agents, Health & Wellness, Language, Web / Web 2.0.			Tagged: , , , , , .			

			

				


We introduce a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation to provide contextually relevant social and web search recommendations. Cobot supports the information discovery process by integrating web information retrieval along with proactive connections to relevant users who can participate in real-time conversations. We describe the conversational framework and report on some preliminary experiments in the system.


Read the paper:


Conversational Framework for Web Search and Recommendations


by Saurav Sahay, Ashwin Ram


ICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.


www.cc.gatech.edu/faculty/ashwin/papers/er-10-01.pdf



			

		


	
		

			

				4
				Sep
			

			
iReMedI – Intelligent Retrieval from Medical Information

			

			Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0.			Tagged: , , , , .			

			

				
Effective encoding of information is one of the keys to qualitative problem solving. Our aim is to explore Knowledge Representation techniques that capture meaningful word associations occurring in documents. We have developed iReMedI, a TCBR-based problem solving system as a prototype to demonstrate our idea. For representation we have used a combination of NLP and graph based techniques which we call as Shallow Syntactic Triples, Dependency Parses and Semantic Word Chains. To test their effectiveness we have developed retrieval techniques based on PageRank, Shortest Distance and Spreading Activation methods. The various algorithms discussed in the paper and the comparative analysis of their results provides us with useful insight for creating an effective problem solving and reasoning system.


Read the paper:


iReMedI – Intelligent Retrieval from Medical Information


by Saurav Sahay, Bharat Ravisekar, Anu Venkatesh, Sundaresan Venkatasubramanian, Priyanka Prabhu, Ashwin Ram


9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany


www.cc.gatech.edu/faculty/ashwin/papers/er-08-05.pdf

			

		


	
		

			

				1
				Jan
			

			
Interactive Case-Based Reasoning for Precise Information Retrieval

			

			Posted by cognitivecomputing in Language, Learning, Web / Web 2.0.			Tagged: , , , .			

			

				
The knowledge explosion has continued to outpace technological innovation in search engines and knowledge management systems. It is increasingly difficult to find relevant information, not just on the World Wide Web at large but even in domain- specific medium-sized knowledge bases—online helpdesks, maintenance records, technical repositories, travel databases, e-commerce sites, and many others. Despite advances in search and database technology, the average user still spends inordinate amounts of time looking for specific information needed for a given task.


This paper describes an adaptive system for the precise, rapid retrieval and synthesis of information from medium-sized knowledge bases in response to problem-solving queries from a diverse user population. We advocate a shift in perspective from “search” to “answers. Instead of returning dozens or hundreds of hits to a user, the system should attempt to find answers that may or may not match the query directly but are relevant to the user’s problem or task.


This problem has been largely overlooked as research has tended to concentrate on techniques for broad searches of large databases over the Internet (as exemplified by Google) and structured queries of well-defined databases (as exemplified by SQL). However, the problem discussed in this chapter is sufficiently different from these extremes to both present a novel set of challenges as well as provide a unique opportunity to apply techniques not traditionally found in the information retrieval literature. Specifically, we discuss an innovative combination of techniques‚ case-based reasoning coupled with text analytics‚ to solve the problem in a practical, real-world context.


We are interested in applications in which users must quickly retrieve answers to specific questions or problems from a complex information database with a minimum of effort and interaction. Examples include internal helpdesk support, web-based self-help for consumer products, decision-aiding systems for support personnel, and repositories for specialized documents such as patents, technical documents, or scientific literature. These applications are characterized by the fact that a diverse user population accesses highly focused knowledge bases in order to find precise answers to specific questions or problems. Despite the growing popularity of on-line service and support facilities for internal use by employees and for external use for customers, most such sites rely on traditional search engine technologies and are not very effective in reducing the time, expertise, and complexity required on the user’s part.


Read the paper:


Interactive Case-Based Reasoning for Precise Information Retrieval


by Ashwin Ram, Mark Devaney


In Case-Based Reasoning in Knowledge Discovery and Data Mining, David Aha and Sankar Pal (editors). 


www.cc.gatech.edu/faculty/ashwin/papers/er-05-02.pdf

			

		


	
		

			

				15
				May
			

			
A Functional Theory of Creative Reading: Process, Knowledge, and Evaluation

			

			Posted by cognitivecomputing in Language, Learning.			Tagged: , , , , .			

			

				
Reading is a complex cognitive behavior, making use of dozens of tasks to achieve comprehension. As such, it represents an important aspect of general cognition; the benefits of having a theory of reading would be far-reaching. Additionally, there is an aspect of reading which has been largely ignored by the research, namely, reading appears to encompass a creative process. In this dissertation, I present a theory capable of explaining creative reading. There are not separate reading behaviors, some mundane and some creative; instead, all of reading must be understood as a creative process. Therefore, a comprehensive theory of reading and creativity is needed. Unfortunately, although the scientific study of reading has been undertaken for almost a century, it is often done in a piecemeal fashion–that is, the research has often concentrated on a narrow aspect of reading behavior. This is due, to some degree, to the fact that reading is a huge process–however, it is my belief that failing to consider the complete reading process will limit the research, Thus, in my work, I identify a set of tasks which sufficiently covers the reading process for short narratives. Together, these tasks form the basis of a functional theory of reading.


Using the reading framework to support the research, I produced a theory of creative understanding, which is the process by which novel concepts come to be understood by a reasoner. To accomplish this, I created a taxonomy of novelty types, I produced a knowledge representation and ontology of sufficient flexibility to permit the representation of a wide-range of conceptual forms, and I created an interlocking set of four tasks which act together to produce the behavior–memory retrieval, analogical mapping, base-constructive analogy, and problem reformulation. My technique for base-constructive analogy is one of the more unique features of my work; it permits existing concepts to be combined in ways which enable novel concepts to be understood. In addition to that, the theory provides for reasonable bounding to occur on the process of creative understanding through a set of heuristics associated with the ontology. This allows reasonable bounding to occur while greatly reducing the possibility of non-useful understandings.


The theory of creative reading is instantiated in a computer model, the ISAAC system, which reads and comprehends short science fiction stories. The model has allowed me to perform empirical evaluation, providing an important stage in the overall theory revision cycle. The evaluation demonstrated that ISAAC can answer independently-generated comprehension questions about a set of science fiction stories with skill comparable to a group of college students. This result, along with an analysis of the internal workings of the model enables me to claim that my theory of creative reading is sufficient to explain important aspects of the behavior.


Read the thesis:


A functional theory of creative reading: Process, knowledge, and evaluation


by Kenneth Moorman


PhD Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1997


www.cs.transy.edu/kmoorman/Dissertation/

			

		


	
		

			

				15
				Oct
			

			
Structuring On-The-Job Troubleshooting Performance to Aid Learning

			

			Posted by cognitivecomputing in Education.			Tagged: , , , , , .			

			

				
This paper describes a methodology for aiding the learning of troubleshooting tasks in the course of an engineer’s work. The approach supports learning in the context of actual, on-the-job troubleshooting and, in addition, supports performance of the troubleshooting task in tandem. This approach has been implemented in a computer tool called WALTS (Workspace for Aiding and Learning Troubleshooting).


This method aids learning by helping the learner structure his or her task into the conceptual components necessary for troubleshooting, giving advice about how to proceed, suggesting candidate hypotheses and solutions, and automatically retrieving cognitively relevant media. WALTS includes three major components: a structured dynamic workspace for representing knowledge about the troubleshooting process and the device being diagnosed; an intelligent agent that facilitates the troubleshooting process by offering advice; and an intelligent media retrieval tool that automatically presents candidate hypotheses and solutions, relevant cases, and various other media. WALTS creates resources for future learning and aiding of troubleshooting by storing completed troubleshooting instances in a self-populating database of troubleshooting cases.


The methodology described in this paper is partly based on research in problem-based learning, learning by doing, case-based reasoning, intelligent tutoring systems, and the transition from novice to expert. The tool is currently implemented in the domain of remote computer troubleshooting.


Read the paper:


Structuring On-The-Job Troubleshooting Performance to Aid Learning


by Brian Minsk, Hari Balakrishnan, Ashwin Ram


World Conference on Engineering Education, Minneapolis, MN, October 1995


www.cc.gatech.edu/faculty/ashwin/papers/er-95-06.pdf

			

		


	
		

			

				1
				May
			

			
AQUA: Questions that Drive the Explanation Process

			

			Posted by cognitivecomputing in Language, Learning.			Tagged: , , , .			

			

				
Editors’ Introduction:


In the doctoral disseration from which this chapter is drawn, Ashwin Ram presents an alternative perspective on the processes of story understanding, explanation, and learning. The issues that Ram explores in that dissertation are similar to those that are explored by the other authors in this book, but the angle that Ram take on these issues is somewhat different. His exploration of these processes is organized around the central theme of question asking. For him, understanding a story means identifying questions that the story raises, and questions that it answers.


Question asking also serves as a lens through which each of the sub-processes of is viewed: the retrieval of stored explanations, for instance, is driven by a library of what Ram calls “XP retrieval questions”; likewise, evaluation is driven by another set of questions, called “hypothesis verification questions”.


The AQUA program, which is Ram’s implementation of this question-based theory of understanding, is a very complex system, probably the most complex among the programs described in this book. AQUA covers a great deal of ground; it implements the entire case-based explanation process in a question-based manner. In this chapter, Ram focuses on the high-level description of the questions the programs asks, especially the questions it asks when constructing and evaluating explanations of volitional actions.


Read the paper:


AQUA: Questions that Drive the Explanation Process


by Ashwin Ram


In Inside Case-Based Explanation, R.C. Schank, A. Kass, and C.K. Riesbeck (eds.), 207-261, Lawrence Erlbaum, 1994. 


www.cc.gatech.edu/faculty/ashwin/papers/git-cc-93-47.pdf

			

		


	
		

			

				1
				Jan
			

			
Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases

			

			Posted by cognitivecomputing in Learning.			Tagged: , , , .			

			

				
This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner’s memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good “lessons” to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices.


We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.


Read the paper:


Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases


by Ashwin Ram


Machine Learning journal, 10:201-248, 1993


www.cc.gatech.edu/faculty/ashwin/papers/git-cc-92-03.pdf

			

		


	
		

			
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