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Notice

AISB event Bulletin Item

CALL FOR PAPERS: Workshop on Bayesian Natural Language Semantics and Pragmatics, 5-9 Aug 2013, Duesseldorf, GERMANY

http://www.bnlsp.ws

Aims and Scope

Bayesian interpretation is a standard technique in signal interpretation in 
which the most probable message M conveyed by a signal S is found by using two 
models, namely the prior probability of the message M and the production 
probability of the signal S, that is, the probability of the signal given the 
message. Since by Bayes' theorem argmaxM p(M|S) = argmaxM p(M)p(S|M), the two 
models suffice for detecting the most probable message given the signal. 
Bayesian NL interpretation is just the same: the signal is an utterance (of a 
word, sentence, turn or text), and the messages, that is the interpretation 
hypotheses range over the possible intentions of the speaker, which according 
to Grice the hearer must recognise in a successful communication. Bayesian 
methods include Bayesian nets, Bayesian belief revision and information states 
that are represented as probability distributions, among other methods.

The workshop wants to collect emerging work in Bayesian interpretation as well 
as work using Bayesian methods in natural language (NL) interpretation and 
bring together the various approaches so as to contribute to a more integrated 
research programme in this new area.

What we are looking for are analyses in semantics and pragmatics using the 
possibilities of Bayesian interpretation, and papers exploring the consequences 
of Bayesian NL interpretation. An example of the first kind is the analysis of 
counterfactuals pioneered by Judea Pearl and elaborated in a more linguistic 
setting by Stefan Kaufmann and Katrin Schulz, an approach to causality that 
Lassiter and Zeevat show also to apply to presupposition projection. The second 
kind is exemplified by Jayez and Winterstein in their analysis of argumentation 
or in interpretation by abduction (Hobbs et al.).

Bayesian interpretation gives three departures from standard assumptions. 
First, it can be seen as a defence of linguistic semantics as a production 
system that maps meanings into forms as was assumed in generative semantics, 
but also in systemic grammar, functional grammar and optimality theoretic 
syntax. This brings with it a more relaxed view of the relation between 
syntactic and semantic structure: the mapping from meanings to forms should be 
efficient (linear) and the prior strong enough to find the inversion from the 
cues in the utterance.

The second departure is that the prior is also the source for what is not said 
in the utterance but part of the pragmatic enrichment of the utterance: what is 
part of the speaker intention but not of the literal meaning. There is no 
principled difference between inferring in perception that the man who is 
running in the direction of the bus stop as the bus is approaching is trying to 
catch the bus and inferring in conversation that the man who states that he is 
out of petrol is asking for help with his problem.

The third departure is thus that interpretation is viewed as a stochastic and 
holistic process leading from stochastic data to a symbolic representation or a 
probability distribution over such representations that can be equated with the 
conversational contribution of the utterance.

Models relevant to the prior â?? that is, the probability of the message M 
â?? include Bayesian networks for causality, association between concepts 
and (common ground) expectations. It is tempting to see a division in logic: 
classical logic for expressing the message, the logic of uncertainty for 
finding out what those messages are. Radical Bayesian interpretation can be 
described as the view that not just the identification of the message requires 
Bayesian methods, but also the message itself and the contextual update have to 
be interpreted with reference to Bayesian belief revision, Bayesian networks or 
conceptual association. It states the hypothesis of a Bayesian mind/ brain. 
(Cf. Oaksford and Chater.)

Bayesian NL interpretation can be defined as stochastic interpretation based on 
a model of NL production and the prior probabilities about what the speaker 
will do in the context and the other events in the context, as in speech 
perception, where the model of speech production is a Hidden Markov Model and 
the prior probabilities are given by a language model, or as in computer vision 
where the production model is the mental camera that maps hypotheses about what 
is seen to visual signals and the required prior is given in much the same way 
as in NL. (NL interpretation and vision share crucial features â?? being 
fast, subconscious and eliminating vast amounts of ambiguities â?? that are 
not covered by standard pipeline models of NLI.) Bayesian interpretation merely 
brings NL interpretation in line with these popular views of how these other 
kinds of perception might work. It is however clear that a range of classical 
techniques are important constraints on the prior probability (e.g. classical 
logical entailment and conversational and other planning) and on the production 
model (e.g. rule based grammar).

The topic of the workshop can also be approached from cognitive science, as the 
application to language of the increasingly popular hypothesis that the mind is 
a Bayesian inference machine (Oaksford and Chater). It follows from that view 
that natural language interpretation, the information states that it relies on 
and constructs and the semantics of expressions of inference like conditionals 
and modals must be Bayesian.

Topics of interest include (but are not limited to) the following:

- Bayesian intention recognition
- evaluating syntax by simulated production
- motor theories of recognition and interpretation
- natural language interpretation and vision
- the Bayesian mind/ brain
- information states as probability distributions
- causal reasoning
- interpretation by (weighted) abduction
- Bayesian models of relevance
- Bayesian accounts of semantic and pragmatic phenomena such as metonymy, 
pronoun resolution, discourse structure detection, temporal interpretation, 
noun-noun compounds, particle meaning, vagueness and others


References

- Dehghani, M., Iliev, R., and Kaufmann, S. 2012. Causal explanation and fact 
mutability in counterfactual reasoning. Mind & Language 27(1):55-85.
- Hobbs, J., Stickel, M., Appelt, D., and Martin, P. (1990). Interpretation as 
abduction. Technical Report 499, SRI International, Menlo Park, California.
- Jayez, J., and Winterstein, G. 2012. Additivity and Probabilty. Lingua. 
http://dx.doi.org/ 10.1016/j.lingua.2012.11.004
- Lassiter, D. 2012. Presuppositions, provisos, and probability. Semantics and 
Pragmatics, 5{2}: 1-37.
- Oaksford, M., Chater, N. 2010. Cognition and Conditionals: Probability and 
Logic in Human Thinking. OUP.
- Pearl, J. 2009. Causality. 2nd edition. CUP.
- Schulz, K. 2011. "If you'd wiggled A, the B would've changed", Causality and 
counterfactual conditionals. Synthese 179(2).
- Zeevat, H. 2013. Accommodation in Communication. Ms.

Further introductory texts on Bayesian models of cognition:

- Tenenbaum,J., Kemp, C., Griffiths, T., Goodman, N. 2011. How to grow a mind: 
Structure, statistics, and abstraction. Science. 
http://www.stanford.edu/~ngoodman/papers/tkgg-science11-reprint.pdf
- Griffiths, T., Kemp, C., Tenenbaum, J. 2008. Bayesian models of cognition. In 
The Cambridge Handbook of Computational Cognitive Modeling. 
http://cocosci.berkeley.edu/tom/papers/bayeschapter.pdf
- Trends in Cognitive Science, Special issue 2006 on probabilistic models of 
cognition including an article on probabilistic models of language acquisition 
and processing by Nick Chater and Christopher Manning: 
http://www.cell.com/trends/cognitive-sciences/issue?pii=S1364-6613(06)X0119-5.


Submission Details

Authors are invited to submit an anonymous, extended abstract. Submissions 
should not exceed 2 pages, including references. Submissions should be in PDF 
format. Please submit your abstract via the EasyChair system: 
https://www.easychair.org/conferences/?conf=bnlsp13. For questions regarding 
the submission procedure, contact Hans-Christian Schmitz (v.i.). The 
submissions will be reviewed by the workshop's programme committee. 
Contributors will be invited for a discussion session on the Future of Bayesian 
NL Interpretation scheduled for the Saturday after the workshop.


Workshop Format

The workshop is part of ESSLLI and is open to all ESSLLI participants. It will 
consist of five 90-minute sessions held over five consecutive days in the first 
week of ESSLLI. There will be 2-3 slots for paper presentation and discussion 
per session. On the first day the workshop organisers will give an introduction 
to the topic. Proceedings: Workshop Proceedings will be published. We will 
publish a separate call for full papers.


Invited Speakers

- t.b.a.


Important Dates

- Submission Deadline: April 15, 2013
- Notification: April 30, 2013
- Preliminary programme: May 7, 2013
- Workshop dates: August 5-9, 2012


Programme Committee

- Anton Benz, ZAS Berlin
- Graeme Forbes, University of Colorado, Boulder
- Fritz Hamm, Universit??t T??bingen
- Jerry Hobbs, University of Southern California
- Noah Goodman, Stanford University
- Jacques Jayez, ENS Lyon, CNRS L2C2
- Stefan Kaufmann, Northwestern University & University of Connecticut
- Uwe Kirschenmann, Fraunhofer FIT
- Ewan Klein, University of Edinburgh
- Daniel Lassiter, Stanford University
- David Schlangen, Universit??t Bielefeld
- Hans-Christian Schmitz, Universit??t Duisburg-Essen
- Markus Schrenk, Universit??t K?¶ln & Universit??t D??sseldorf
- Bernhard Schr?¶der, Universit??t Duisburg-Essen
- Gr?©goire Winterstein, CNRS LLF
- Henk Zeevat, ILLC Amsterdam
- Thomas Ede Zimmermann, Universit??t Frankfurt am Main


Organisers

- Hans-Christian Schmitz, Universit??t Duisburg-Essen, 
hans-christian.schmitz@uni-due.de
- Henk Zeevat, ILLC Amsterdam, H.W.Zeevat@uva.nl