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

Don’t Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation

We introduce a reinforcement learning-based approach to simultaneous ma-chine translation—producing a trans-lation while receiving input words— between languages with drastically dif-ferent word orders: from verb-final lan-guages (e.g., German) to verb-medial languages (English). In traditional ma-chine translation, a translator must “wait ” for source material to appear be-fore translation begins. We remove this bottleneck by predicting the final verb in advance. We use reinforcement learn-ing to learn when to trust predictions about unseen, future portions of the sentence. We also introduce an evalua-tion metric to measure expeditiousness and quality. We show that our new translation model outperforms batch and monotone translation strategies.

CLiFF Notes: 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, Psychology, 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. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse

COMMITMENT AND FLEXIBILITY IN THE DEVELOPING PARSER

This dissertation investigates adults and children's sentence processing mechanisms, with a special focus on how multiple levels of linguistic representation are incrementally computed in real time, and how this process affects the parser's ability to later revise its early commitments. Using cross-methodological and cross-linguistic investigations of long-distance dependency processing, this dissertation demonstrates how paying explicit attention to the procedures by which linguistic representations are computed is vital to understanding both adults' real time linguistic computation and children's reanalysis mechanisms. The first part of the dissertation uses time course evidence from self-paced reading and eye tracking studies (reading and visual world) to show that long-distance dependency processing can be decomposed into a sequence of syntactic and interpretive processes. First, the reading experiments provide evidence that suggests that filler-gap dependencies are constructed before verb information is accessed. Second, visual world experiments show that, in the absence of information that would allow hearers to predict verb content in advance, interpretive processes in filler-gap dependency computation take around 600ms. These results argue for a predictive model of sentence interpretation in which syntactic representations are computed in advance of interpretive processes. The second part of the dissertation capitalizes on this procedural account of filler-gap dependency processing, and reports cross-linguistic studies on children's long-distance dependency processing. Interpretation data from English and Japanese demonstrate that children actively associate a fronted wh-phrase with the first VP in the sentence, and successfully retract such active syntactic commitments when the lack of felicitous interpretation is signaled by verb information, but not when it is signaled by syntactic information. A comparison of the process of anaphor reconstruction in adults and children further suggests that verb-based thematic information is an effective revision cue for children. Finally, distributional analyses of wh-dependencies in child-directed speech are conducted to investigate how parsing constraints impact language acquisition. It is shown that the actual properties of the child parser can skew the input distribution, such that the effective distribution differs drastically from the input distribution seen from a researcher's perspective. This suggests that properties of developing perceptual mechanisms deserve more attention in language acquisition research

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

One concern of the Computer Graphics Research Lab is in simulating human task behavior and understanding why the visualization of the appearance, capabilities and performance of humans is so challenging. Our research has produced a system, called Jack, for the definition, manipulation, animation and human factors analysis of simulated human figures. Jack permits the envisionment of human motion by interactive specification and simultaneous execution of multiple constraints, and is sensitive to such issues as body shape and size, linkage, and plausible motions. Enhanced control is provided by natural behaviors such as looking, reaching, balancing, lifting, stepping, walking, grasping, and so on. Although intended for highly interactive applications, Jack is a foundation for other research. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object around us, and yet the most structurally complex. Their everyday movements are amazingly fluid, yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language. Present technology lets us approach human appearance and motion through computer graphics modeling and three dimensional animation, but there is considerable distance to go before purely synthesized figures trick our senses. We seek to build computational models of human like figures which manifest animacy and convincing behavior. Towards this end, we: Create an interactive computer graphics human model; Endow it with reasonable biomechanical properties; Provide it with human like behaviors; Use this simulated figure as an agent to effect changes in its world; Describe and guide its tasks through natural language instructions. There are presently no perfect solutions to any of these problems; ultimately, however, we should be able to give our surrogate human directions that, in conjunction with suitable symbolic reasoning processes, make it appear to behave in a natural, appropriate, and intelligent fashion. Compromises will be essential, due to limits in computation, throughput of display hardware, and demands of real-time interaction, but our algorithms aim to balance the physical device constraints with carefully crafted models, general solutions, and thoughtful organization. The Jack software is built on Silicon Graphics Iris 4D workstations because those systems have 3-D graphics features that greatly aid the process of interacting with highly articulated figures such as the human body. Of course, graphics capabilities themselves do not make a usable system. Our research has therefore focused on software to make the manipulation of a simulated human figure easy for a rather specific user population: human factors design engineers or ergonomics analysts involved in visualizing and assessing human motor performance, fit, reach, view, and other physical tasks in a workplace environment. The software also happens to be quite usable by others, including graduate students and animators. The point, however, is that program design has tried to take into account a wide variety of physical problem oriented tasks, rather than just offer a computer graphics and animation tool for the already computer sophisticated or skilled animator. As an alternative to interactive specification, a simulation system allows a convenient temporal and spatial parallel programming language for behaviors. The Graphics Lab is working with the Natural Language Group to explore the possibility of using natural language instructions, such as those found in assembly or maintenance manuals, to drive the behavior of our animated human agents. (See the CLiFF note entry for the AnimNL group for details.) Even though Jack is under continual development, it has nonetheless already proved to be a substantial computational tool in analyzing human abilities in physical workplaces. It is being applied to actual problems involving space vehicle inhabitants, helicopter pilots, maintenance technicians, foot soldiers, and tractor drivers. This broad range of applications is precisely the target we intended to reach. The general capabilities embedded in Jack attempt to mirror certain aspects of human performance, rather than the specific requirements of the corresponding workplace. We view the Jack system as the basis of a virtual animated agent that can carry out tasks and instructions in a simulated 3D environment. While we have not yet fooled anyone into believing that the Jack figure is real , its behaviors are becoming more reasonable and its repertoire of actions more extensive. When interactive control becomes more labor intensive than natural language instructional control, we will have reached a significant milestone toward an intelligent agent

DFKI Workshop on Natural Language Generation

On the Saarbrücken campus sites as well as at DFKI, many research activities are pursued in the field of Natural Language Generation (NLG). We felt that too little is known about the total of these activities and decided to organize a workshop in order to share ideas and promote the results. This DFKI workshop brought together local researchers working on NLG. Several papers are co-authored by international researchers. Although not all NLG activities are covered in the present document, the papers reviewed for this workshop clearly demonstrate that Saarbrücken counts among the important NLG sites in the world