Calls

Call for Regular Papers

It is our pleasure to inform you about ICGI 2020/21, the major forum for presentation and discussion of original research papers on all aspects of grammar learning. ICGI, which has been organized bi-annually since the early nineties, will be held on-line in August 2021 after being postponed due to COVID-19 last year.

ICGI 2021 is the place to present your work on learning formal grammars, finite state machines, context-free grammars, Markov models, or any models related to language theory, stochastic or otherwise. Both theoretical work and experimental analyses are welcomed as submissions. This year we especially encourage submissions related to connectionist models such as neural networks, since there are tutorials scheduled on that topic. 

The conference will be spread out over August 23rd-27th, featuring a mix of live talks, asynchronous video presentations, tutorials, and an online competition. There will be one synchronous event per day plus dedicated time for each accepted paper; further details will be announced by email.

Topics of interest

• Theoretical aspects of grammatical inference: learning paradigms, learnability results, complexity of learning.
• Empirical and theoretical research on query learning, active learning, and other interactive learning paradigm
• Empirical and theoretical research on methods using or including, but not limited to, spectral learning, state-merging, distributional learning, statistical relational learning, statistical inference and/or Bayesian learning
• Learning algorithms for language classes inside and outside the Chomsky hierarchy. Learning tree and graph grammars. 
• Learning probability distributions over strings, trees or graphs, or transductions thereof.
• Learning with contextualized data: for instance, Grammatical inference from strings or trees paired with semantics representations, or learning by situated agents and robots.
• Experimental and theoretical analysis of different approaches to grammar induction, including artificial neural networks, statistical methods, symbolic methods, information-theoretic approaches, minimum description length, complexity-theoretic approaches, heuristic methods, etc.
• Novel approaches to grammatical inference: induction by DNA computing or quantum computing, evolutionary approaches, new representation spaces, etc.
• Successful applications of grammatical learning to tasks in fields including, but not limited to, natural language processing and computational linguistics, model checking and software verification, bioinformatics, robotic planning and control, and pattern recognition.

Types of contributions

We welcome three types of papers:

• Formal and/or technical papers describe original solutions (theoretical, methodological or conceptual) in the field of grammatical inference. A technical paper should clearly describe the situation or problem tackled, the relevant state of the art, the position or solution suggested and the benefits of the contribution.
• Position papers can describe completely new research position, or approaches, or open problems. Current limits can be discussed. In all cases rigor in presentation will be required. Such papers must describe precisely the situation, problem, or challenge addressed, and demonstrate how current methods, tools, ways of reasoning, may be inadequate.
• Tool papers describing a new tool for grammatical inference. The tool must be publicly available and the paper has to contain several use-case studies describing the use of the tool. In addition, the paper should clearly describe the implemented algorithms, input parameters and syntax, and the produced output.

Guidelines for authors

Accepted papers will be published within the Proceedings of Machine Learning Research series (http://proceedings.mlr.press/). They must be submitted in pdf format through EasyChair. The total length of the paper should not exceed 12 pages on A4-size paper (references and appendix may exceed this limit but Authors are warned that Reviewers may not read after page 12). The prospective authors are strongly recommended to use the JMLR style file for LaTeX (https://ctan.org/tex-archive/macros/latex/contrib/jmlr) since it will be the required format of final published version.

The peer review process is double-blind: we expect submitted papers to be anonymous.

Timeline

Conference Chairs:

Jane Chandlee, Haverford College
Rémi Eyraud, QARMA team, Aix-Marseille University
Jeffrey Heinz, Stony Brook University
Adam Jardine, Rutgers University
Menno van Zaanen, South African Centre for Digital Language Resources