Jon Juett April 21, 2014

 Selected very recent papers  Includes some student level event / conference papers  UM Health Counseling Program  Correctly respond to emotion  Follow tree-based conversation  Detect deception, infer progress, etc.

 First automated system by Green et al in 1961  Early systems domain specific  Open domain problem initially too large  Internet makes more data sets available and searches more common  Database querying becomes faster  Open domain QA research resumes

 Computationally inexpensive  Current bottleneck in initial search  Can scale with dataset growth  Especially Internet  Fully automated  Except if cooperative steps with user included  Finds short, correct answers  User-friendly

 Like a Google Search  Uses primitive techniques  Bag of Words  Easier task  Returns entire likely relevant documents  Leaves answer extraction step to user

 In an open domain, cannot rely on domain specific knowledge  General world knowledge only  Conglomeration of components  Many systems overlap, plug-in techniques

 Cooperative Component  Not restricted to literal answers  Allows clarification of question for better answer  Can build up information on user interests, system may even lead conversation to do this

 Database approaches  Semantic via Resource Description Framework (RDF) Triples ▪ (subject, verb, object) ▪ Answer true/false or factoid ▪ Efficiently increase answer database  Template matching ▪ Use machine learning to cluster questions ▪ Templates can correct for typos and abbreviations ▪ Only need to store an answer to each cluster ▪ Novel questions matched to closest cluster

Question WordGoogle Answer RatioSemantic Web Answer Ratio What When Where Which Who How0.50 How old How long0.98 How many0.50 Najmi et al, 2013

 Response to unnatural feel of non-affective dialogue systems  Sense user emotional state  Punctuation, word choice, capitalization, emoticons  Simulate emotions  Speech synthesis, emoticons, nonverbal behavior  Attempt to influence user emotion  Extend to poll online communities  Composed of perception layer, control layer, and actuator-communicator layer

 Complex automated evaluation beyond state of the art  Multi-step reasoning  Real-time question answering  Language of user, query, data  Answer extraction  Customize to user preferences

 Fader, Anthony, Zettlemoyer, Luke, Etzioni, Oren, "Paraphrase-Driven Language for Open Question Answering," University of Washington ACL  Melo, Dora, Rodriues, Irene Pimenta, Nogueira, Vitor Beires, "A Review on Cooperative Question-Answering Systems,"  Najmi, E.; Hashmi, K.; Khazalah, F.; Malik, Z., "Intelligent Semantic Question Answering System," Cybernetics (CYBCONF), 2013 IEEE International Conference on, vol., no., pp.255,260, June 2013  Skowron, Marcin, "Affect Listeners: Acquisition of Affective States by Means of Conversational Systems," Development of Multimodal Interfaces: Active Listening and Synchrony, Springer Berlin Heidelberg, pp January  Tyagi, Deepali, Joshi, Tejas, Ghule, Dhanashri, Ameya, Joshi, "An Interactive Answering System using Template Matching and SQL Mapping for Natural Language Processing," International Journal of Advance Research in Computer Science and Management Studies, vol. 2, issue 2, pp , February 2014