Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

CLIFF is the Computational Linguists\u27 Feedback Forum. We are a group of students and faculty who gather once a week to hear a presentation and discuss work currently in progress. The \u27feedback\u27 in the group\u27s name is important: we are interested in sharing ideas, in discussing ongoing research, and in bringing together work done by the students and faculty in Computer Science and other departments. However, there are only so many presentations which we can have in a year. We felt that it would be beneficial to have a report which would have, in one place, short descriptions of the work in Natural Language Processing at the University of Pennsylvania. This report then, is a collection of abstracts from both faculty and graduate students, in Computer Science, Psychology and Linguistics. We want to stress the close ties between these groups, as one of the things that we pride ourselves on here at Penn is the communication among different departments and the inter-departmental work. Rather than try to summarize the varied work currently underway at Penn, we suggest reading the abstracts to see how the students and faculty themselves describe their work. The report illustrates the diversity of interests among the researchers here, as well as explaining the areas of common interest. In addition, since it was our intent to put together a document that would be useful both inside and outside of the university, we hope that this report will explain to everyone some of what we are about

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

Music adapting to the brain: From diffusion chains to neurophysiology

During the last decade, the use of experimental approaches on cultural evolution research has provided novel insights, and supported theoretical predictions, on the principles driving the evolution of human cultural systems. Laboratory simulations of language evolution showed how general-domain constraints on learning, in addition to pressures for language to be expressive, may be responsible for the emergence of linguistic structure. Languages change when culturally transmitted, adapting to fit, among all, the cognitive abilities of their users. As a result, they become regular and compressed, easier to acquire and reproduce. Although a similar theory has been recently extended to the musical domain, the empirical investigation in this field is still scarce. In addition, no study to our knowledge directly addressed the role of cognitive constraints in cultural transmission with neurophysiological investigation. In my thesis I addressed both these issues with a combination of behavioral and neurophysiological methods, in three experimental studies. In study 1 (Chapter 2), I examined the evolution of structural regularities in artificial melodic systems while they were being transmitted across individuals via coordination and alignment. To this purpose I used a new laboratory model of music transmission: the multi-generational signaling games (MGSGs), a variant of the signaling games. This model combines classical aspects of lab-based semiotic models of communication, coordination and interaction (horizontal transmission), with the vertical transmission across generations of the iterated learning model (vertical transmission). Here, two-person signaling games are organized in diffusion chains of several individuals (generations). In each game, the two players (a sender and a receiver) must agree on a common code - here a miniature system where melodic riffs refer to emotions. The receiver in one game becomes the sender in the next game, possibly retransmitting the code previously learned to another generation of participants, and so on to complete the diffusion chain. I observed the gradual evolution of several structures features of musical phrases over generations: proximity, continuity, symmetry, and melodic compression. Crucially, these features are found in most of musical cultures of the world. I argue that we tapped into universal processing mechanisms of structured sequence processing, possibly at work in the evolution of real music. In study 2 (Chapter 3), I explored the link between cultural adaptation and neural information processing. To this purpose, I combined behavioral and EEG study on 2 successive days. I show that the latency of the mismatch negativity (MMN) recorded in a pre-attentive auditory sequence processing task on day 1, predicts how well participants learn and transmit an artificial tone system with affective semantics in two signaling games on day 2. Notably, MMN latencies also predict which structural changes are introduced by participants into the artificial tone system. In study 3 (Chapter 4), I replicated and extended behavioral and neurophysiological findings on the temporal domain of music, with two independent experiments. In the first experiment, I used MGSGs as a laboratory model of cultural evolution of rhythmic equitone patterns referring to distinct emotions. As a result of transmission, rhythms developed a universal property of music structure, namely temporal regularity (or isochronicity). In the second experiment, I anchored this result with neural predictors. I showed that neural information processing capabilities of individuals, as measured with the MMN on day 1, can predict learning, transmission, and regularization of rhythmic patterns in signaling games on day 2. In agreement with study 2, I observe that MMN brain timing may reflect the efficiency of sensory systems to process auditory patterns. Functional differences in those systems, across individuals, may produce a different sensitivity to pressures for regularities in the cultural system. Finally, I argue that neural variability can be an important source of variability of cultural traits in a population. My work is the first to systematically describe the emergence of structural properties of melodic and rhythmic systems in the laboratory, using an explicit game-theoretic model of cultural transmission in which agents freely interact and exchange information. Critically, it provides the first demonstration that social learning, transmission, and cultural adaptation are constrained and driven by individual differences in the functional organization of sensory systems

Generative theatre of totality

Generative art can be used for creating complex multisensory and multimedia experiences within predetermined aesthetic parameters, characteristic of the performing arts and remarkably suitable to address Moholy-Nagy's Theatre of Totality vision. In generative artworks the artist will usually take on the role of an experience framework designer, and the system evolves freely within that framework and its defined aesthetic boundaries. Most generative art impacts visual arts, music and literature, but there does not seem to be any relevant work exploring the cross-medium potential, and one could confidently state that most generative art outcomes are abstract and visual, or audio. It is the goal of this article to propose a model for the creation of generative performances within the Theatre of Totality's scope, derived from stochastic Lindenmayer systems, where mapping techniques are proposed to address the seven variables addressed by Moholy-Nagy: light, space, plane, form, motion, sound and man ("man" is replaced in this article with "human", except where quoting from the author), with all the inherent complexities

Animation From Instructions

We believe that computer animation in the form of narrated animated simulations can provide an engaging, effective and flexible medium for instructing agents in the performance of tasks. However, we argue that the only way to achieve the kind of flexibility needed to instruct agents of varying capabilities to perform tasks with varying demands in work places of varying layout is to drive both animation and narration from a common representation that embodies the same conceptualization of tasks and actions as Natural Language itself. To this end, we are exploring the use of Natural Language instructions to drive animated simulations. In this paper, we discuss the relationship between instructions and behavior that underlie our work and the overall structure of our system. We then describe in some what more detail three aspects of the system - the representation used by the Simulator, the operation of the Simulator and the Motion Generators used in the system