A Mission for Computational Natural Language Learning

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 1–5, New York City, June 2006. c⃝2006 Association for Computational Linguistics A Mission for Computational Natural Language Learning Walter Daelemans CNTS Language Technology Group University of Antwerp Belgium walter.daelemans@ua.ac.be Abstract In this presentation, I will look back at 10 years of CoNLL conferences and the state of the art of machine learning of lan- guage that is evident from this decade of research. My conclusion, intended to pro- voke discussion, will be that we currently lack a clear motivation or “mission” to survive as a discipline. I will suggest that a new mission for the field could be found in a renewed interest for theoretical work (which learning algorithms have a bias that matches the properties of language?, what is the psycholinguistic relevance of learner design issues?), in more sophis- ticated comparative methodology, and in solving the problem of transfer, reusabil- ity, and adaptation of learned knowledge. 1 Introduction When looking at ten years of CoNLL conferences, it is clear that the impact and the size of the con- ference has enormously grown over time. The tech- nical papers you will find in this proceedings now are comparable in quality and impact to those of other distinguished conferences like the Conference on Empirical Methods in Natural Language Pro- cessing or even the main conferences of ACL, EACL and NAACL themselves. An important factor in the success of CoNLL has been the continued se- ries of shared tasks (notice we don’t use terms like challenges or competitions) that has produced a use- ful set of benchmarks for comparing learning meth- ods, and that has gained wide interest in the field. It should also be noted, however, that the success of the conferences is inversely proportional with the degree to which the original topics which mo- tivated the conference are present in the programme. Originally, the people driving CoNLL wanted it to be promiscuous (i) in the selection of partners (we wanted to associate with Machine Learning, Lin- guistics and Cognitive Science conferences as well as with Computational Linguistics conferences) and (ii) in the range of topics to be presented. We wanted to encourage linguistically and psycholinguistically relevant machine learning work, and biologically in- spired and innovative symbolic learning methods, and present this work alongside the statistical and learning approaches that were at that time only start- ing to gradually become the mainstream in Compu- tational Linguistics. It has turned out differently, and we should reflect on whether we have become too much of a mainstream computational linguistics conference ourselves, a back-off for the good papers that haven’t made it in EMNLP or ACL because of the crazy rejection rates there (with EMNLP in its turn a back-off for good papers that haven’t made it in ACL). Some of the work targeted by CoNLL has found a forum in meetings like the workshop on Psycho-computational models of human language acquisition, the International Colloquium on Gram- matical Inference, the workshop on …

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