CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning

SIGNLL (pronounce as signal) is the Special Interest Group on Natural Language Learning of the Association for Computational Linguistics (ACL).
The aims and purposes of SIGNLL
Aims
SIGNLL aims to promote research in:
automated acquisition of syntax, morphology and phonology
automated acquisition of semantic / ontological structure
automated acquisition of inter-linguistic correspondences
learning to recognize or produce spoken and written forms
modelling human language acquisition theory and processes
Purview
SIGNLL will foster the application and development of new techniques for the automatic analysis of language, including but not limited to:
case-based, example-based and explanation-based learning
connectionist, statistical and information-theoretic models
inductive, deductive and analytic techniques
clustering and classification procedures
Approaches and Paradigms
SIGNLL emphasizes paradigms which can be exploited automatically:
corpus-based analysis including automated tagging and testing
learning in interactive environments with minimal supervision
automatic preprocessing feeding overtly supervised techniques
unsupervised and naturally / implicitly supervised techniques
Functions and Activities
SIGNLL aims to perform and encourage the following functions and activities:
promotion and development of the field and avenues for publication
provision and coordination of a library of language learning software and data
facilitation of communication between researchers in this field
provision of information about relevant research and resources
development of standard corpora and interface formats for NLL
coordination of the organization of workshops and symposia
liaison with other SIGs, funding organizations, etc.
Natural Language Learning before SIGNLL
Language Learning is the primary focus of a entire subdiscipline of Psychology, viz. Psycholinguistics, and has always been an arena for experimentation in the Artificial Intelligence Machine Learning community. More recently still, relevant work is being pursued in the emerging connectionist movement, and Natural Language Learning (NLL) is in addition proving to be a natural outgrowth of current work in statistical, information-theoretic and corpus-based methods in Computational Linguistics.
In the years before the foundation of SIGNLL, we have seen NLL sessions, workshops and symposia under auspices as varied as COLING (The Unfinished Language, 90), DARPA (90/91), AAAI (MLNLO/CNLP, 91/93), IJCAI (NLL, 91) and ECML (Machine Learning and Text Analysis, 93), although the history of NLL goes back at least a couple of decades.
Events have been held under names as diverse as Natural Language Learning (NLL: reflecting the relationship with the AI subfield of Natural Language), Machine Learning of Natural Language and Ontology (MLNLO: reflecting the intersection with AI subfield of Machine Learning and giving special prominence to the problems of Semantics and Representation), Extraction of Hierarchical Structure (SHOE: emphasizing the development of general hierarchical clustering techniques), Connectionist Natural Language Processing (CNLP), etc.
At the latest of these, the NLL community proposed that we should seek to identify ourselves formally with the ACL as a SIG, and noted that while we are just a source of esoteric applications for ML (primarily concerned with the development of new ML algorithms and their commercial exploitation as Automated Knowledge Acquisition) our aim is by contrast the answering of fundamental scientific questions about the nature of the human language acquisition process and the development of practical NLL techniques for solving current problems across the full range of Computational Linguistics, whilst admitting the widest possible range of computational approaches. Not only does this range encompass machine learning, connectionist, genetic, statistical and information-theoretic methods, but we are prepared also to borrow techniques from further afield (e.g. cryptography, compression and higher criticism).