## Harold Cohen

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## AISB Convention 2016

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## Bishop and AI news

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## AISB YouTube Channel

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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

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 |