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Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017), Gothenburg, 12 - 13 June

Authors Simon Dobnik
Shalom Lappin
ISSN 2002-9764
Publisher CLASP: Centre for Language and Studies in Probability, FLOV, University of Gothenburg
Place of publication Gothenburg, Sweden
Publication year 2017
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Keywords language, logic, machine learning, deep learning, neural networks, computational linguistics, language technology, artificial intelligence
Subject categories Linguistics, Computational linguistics


The past two decades have seen impressive progress in a variety of areas of AI, particularly NLP, through the application of machine learning methods to a wide range of tasks. With the intensive use of deep learning methods in recent years this work has produced significant improvements in the coverage and accuracy of NLP systems in such domains as speech recognition, topic identification, semantic interpretation, and image description generation. While deep learning is opening up exciting new approaches to long standing, difficult problems in computational linguistics, it also raises important foundational questions. Specifically, we do not have a clear formal understanding of why multi-level recursive deep neural networks achieve the success in learning and classification that they are delivering. It is also not obvious whether they should displace more traditional, logically driven methods, or be combined with them. Finally, we need to explore the extent, if any, to which both logical models and machine learning methods offer insights into the cognitive foundations of natural language. The aim of the Conference on Logic and Machine Learning in Natural Language (LAML) was to initiate a dialogue between these two approaches, where they have traditionally remained separate and in competition.

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