Sunday, May 29, 2016

Call for Proposals ESSLLI 2017

Call for Course and Workshop Proposals

29th European Summer School in Logic, Language and Information - ESSLLI 2017
17-28 July, 2017, Toulouse, France


 15 July 2016: Proposal submission deadline
 30 September 2016: Notification

Please submit your proposals here:


Proposals for courses and workshops at ESSLLI 2017 are invited in all areas of Logic, Linguistics and Computing Sciences. Cross-disciplinary and innovative topics are particularly encouraged.

Each course and workshop will consist of five 90 minute sessions, offered daily (Monday-Friday) in a single week. Proposals for two-week courses should be structured and submitted as two independent one-week courses, e.g. as an introductory course followed by an advanced one. In such cases, the ESSLLI programme committee reserves the right to accept just one of the two proposals.

All instructional and organizational work at ESSLLI is performed completely on a voluntary basis, so as to keep participation fees to a minimum. However, organizers and instructors have their registration fees waived, and are reimbursed for travel and accommodation expenses up to a level to be determined and communicated with the proposal notification. ESSLLI can only guarantee reimbursement for at most one course/workshop organizer, and can not guarantee full reimbursement of travel costs for lecturers or organizers from outside of Europe. The ESSLLI organizers would appreciate any help in controlling the School's expenses by seeking complete coverage of travel and accommodation expenses from other sources.

The organizers want to point at the possibility of an EACSL sponsorship, mentioned at the end of this call.


Each proposal should fall under one of the following categories.

Such courses are designed to present the basics of a research area, to people with no prior knowledge in that area. They should be of elementary level, without prerequisites in the course's topic, though possibly assuming a level of general scientific maturity in the relevant discipline. They should enable researchers from related disciplines to develop a level of comfort with the fundamental concepts and techniques of the course's topic, thereby contributing to the interdisciplinary nature of our research community.

Introductory courses are central to ESSLLI's mission. They are intended to introduce a research field to students, young researchers, and other non-specialists, and to foster a sound understanding of its basic methods and techniques. Such courses should enable researchers from related disciplines to develop some comfort and competence in the topic considered. Introductory courses in a cross-disciplinary area may presuppose general knowledge of the related disciplines.

Advanced courses are targeted primarily to graduate students who wish to acquire a level of comfort and understanding in the current research of a field.

Workshops focus on specialized topics, usually of current interest. Workshops organizers are responsible for soliciting papers and selecting the workshop programme. They are also responsible for publishing proceedings if they decide to have proceedings.


Course and workshop proposals should follow closely the following guidelines to ensure full consideration.

Each course proposal can be submitted by no more than two instructors, and each workshop by no more than two organizers. All instructors and organizers must possess a PhD or equivalent degree by the submission deadline.

Course proposals should mention explicitly the intended course category. Proposals for introductory courses should indicate the intended level, for example as it relates to standard textbooks and monographs in the area. Proposals for advanced courses should specify the prerequisites in detail.

Proposals must be submitted in PDF format via:

and include all of the following:

a. Personal information for each proposer: Name, affiliation, contact
   address, email, homepage (optional)

b. General proposal information: Title, category

c. Contents information:
   Abstract of up to 150 words
   Motivation and description (up to two pages)
   Tentative outline
   Expected level and prerequisites
   Appropriate references (e.g. textbooks, monographs, proceedings, surveys)

d. Practical information:
   Relevant preceding meetings and events, if applicable
   Potential external funding for participants


The EACSL offers to act as a sponsor for one course or workshop in the areas of Logic and Computation covered by the Computer Science Logic (CSL) conferences. This course or workshop will be designated an EACSL course/workshop. If you wish to be considered for this, please indicate so on your proposal.


        Shravan Vasishth, Universitaet Potsdam
Local co-chair:
        Philippe Balbiani, IRIT, Toulouse
Language and Computation:
        Sebastian Pado, Universitaet Stuttgart
        Mehrnoosh Sadrzadeh, University of London
Language and Logic:
        Denis Bonnay, l’Universite Paris Ouest
        Jessica Rett, UCLA
Logic and Computation:
        Tomer Kotek, Technische Universitaet Wien
        Anna Zamansky, University of Haifa

Please send any queries you may have to vasishth dot shravan at gmail dot com

Tuesday, November 10, 2015

Understanding underspecification: A comparison of two computational implementations (Logacev et al) accepted in: Quarterly Journal of Experimental Psychology

Pavel Logačev and Shravan Vasishth. Understanding underspecification: A comparison of two computational implementationsQuarterly Journal of Experimental Psychology, 2015. Accepted. [ pdf ]
Swets et al. (2008) present evidence that the so-called ambiguity advantage (Traxler et al., 1998), which has been explained in terms of the Unrestricted Race Model, can equally well be explained by assuming underspecification in ambiguous conditions driven by task-demands. Specifically, if comprehension questions require that ambiguities be resolved, the parser tends to make an attachment: when questions are about superficial aspects of the target sentence, readers tend to pursue an underspecification strategy. It is reasonable to assume that individual differences in strategy will play a significant role in the application of such strategies, so that studying average behavior may not be informative. In order to study the predictions of the good-enough processing theory, we implemented two versions of underspecification: the partial specification model (PSM), which is an implementation of the Swets et al. proposal, and a more parsimonious version, the non-specification model (NSM). We evaluate the relative fit of these two kinds of underspecification to Swets et al.’s data; as a baseline, we also fit three models that assume no underspecification. We find that a model without unspecification pro- vides a somewhat better fit than both underspecification models, while the NSM model provides a better fit than the PSM. We interpret the results as lack of unambiguous evidence in favor of underspecification; however, given that there is considerable existing evidence for good-enough processing in the literature, it is reasonable to assume that some underspecification might occur. Under this assumption, the results can be interpreted as tentative ev- idence for NSM over PSM. More generally, our work provides a method for choosing between models of real-time processes in sentence comprehension that make qualitative predictions about the relationship between several de- pendent variables. We believe that sentence processing research will greatly benefit from a wider use of such methods.

Saturday, October 3, 2015

Some thoughts after attending a conference in Copenhagen

I just got done with a very nice conference in Copenhagen on grammar vs lexicon.

One thing that struck me afresh about several of the people I spoke to there and the talks I heard there is that scientists feel compelled to hold or stand for a theoretical position. People often design their careers around a position that they hold, and then they proceed to defend it no matter what data comes their way. Doing science is very much like a forecasting problem.  Your job is to come up with a prediction of what will happen if a particular experiment is run.

The way we do science, however, is as follows. We first find out what the experiment showed. Then we make the "prediction" based on our favorite theory.  Researchers routinely use the word prediction even when they already know the outcome of an experiment. If this was a weather forecasting problem, it would be like publishing the probability of rain yesterday. Of course you would get everything right! It is this unfortunate tendency to predict after the fact that people are so confident about their theories and positions. After the fact prediction gives an illusion of being right all the time.

I just read a great review of a book on forecasting by the greatest reviewer I have ever encountered on the web: RK, of RK's musings fame.

He discusses a book, Superforecasters, in which the author lays out the qualities of a good forecaster. I quote from the blog almost verbatim:

  • Good back of the envelope calculations
  • Starting with outside view that reduces anchoring bias
  • Subsequent to outside view, get a grip on the inside view
  • Look out for various perspectives about the problem
  • Think three/four times, think deeply to root out confirmation bias
  • It's not the raw crunching power you have that matters most. It's what you do with it.

And here is another quote from the blog, which itself is a quote from the book:

Unpack the question into components. Distinguish as sharply as you can between the known and unknown and leave no assumptions unscrutinized. Adopt the outside view and put the problem into a comparative perspective that downplays its uniqueness and treats it as a special case of a wider class of phenomena. Then adopt the inside view that plays up the uniqueness of the problem. Also explore the similarities and differences between your views and those of others-and pay special attention to prediction markets and other methods of extracting wisdom from crowds. Synthesize all these different views into a single vision as acute as that of a dragonfly. Finally, express your judgment as precisely as you can, using a finely grained scale of probability.

And finally, RK also excerpts a composite portrait of a good forecaster from the book: 

Scientists in psycholinguistics tend to be the exact opposite of a good forecaster. 

They hunker down and defend to death one position, never never never back down in the face of counterevidence, never entertain multiple alternative theories simultaneously, never express any self-doubt (at least not publicly) that their favorite position might be wrong. Whenever we write papers, we end up converging on what we claim is the most plausible explanation for the result we have found. We never end on an equivocation, because that would mean rejection from the top journal we have submitted our paper to.

If anyone other than me is reading this blog, maybe you should read RK's original review of the book, Superforecasters, and maybe also read the book (I know I will), and then think about what's wrong with the way you are doing science, because it is bass-ackwards. We are terrible forecasters, and there's a damn good reason for it!