Integrating Generic and Contextual Knowledge
11/03/2020: Concerning COVID-19: we will decide by March 30th whether we should postpone the GeCKo symposium in light of COVID-19. Please postpone GeCKo travel arrangements until then if possible.
25/02/2020: Paper review period is from 25 Feb to 7 March.
14/02/2020: Submission closed, thanks all!
06/02/2020: ♥ The submission deadline has been extended by 1 week, to Valentine's day: February 14, 2020. ♥
13/01/2020: We are able to provide a limited number of travel grants for student presenters, up to 400 euros each, in the form of post-hoc reimbursement of travel expenses. Details on how to apply will be announced on this website soon.
For both humans and computational models, it is essential to be able to abstract away from specific instances to broader categories, e.g. to build a generic concept for "bird" from instances of birds. But modelling specific situations is equally essential; for instance to be able to understand the sentence "that bird is about to peck you" and react accordingly. Current data-driven models excel at distilling generic knowledge acquired over time, such as knowledge reflected by which words or objects tend to co-occur. However, they can still struggle with specific situations, which require fast recognition of individual elements, such as entities, events, and relationships, and the ability to reason about them. In the literature, the relation between generic and contextual knowledge surfaces in dichotomies such as:
- lexical/conceptual/generic vs. contextual(ized)/grounded/situated/referential/utterance-specific information;
- category/type/kind vs. instance/token;
- semantics vs. pragmatics;
- semantic vs. episodic memory;
- slow vs. fast learning.
The challenge of how to combine generic knowledge with situation-specific information arises in many applications of Computational Linguistics and connected areas and applications, such as Machine Translation, Natural Language Inference, and Language and Vision. We think that it would be beneficial to address this challenge in an integrative fashion, drawing inspiration from across the field. The GeCKo symposium seeks to 1) understand the issues involved in the integration of generic and situation-specific information in Computational Linguistics, across applications and research areas; 2) identify ways forward; and 3) cross-fertilize Computational Linguistics with Machine Learning, Linguistics, and Cognitive Science researchers working at this junction.
The GeCKo symposium will feature talks by invited speakers, contributed talks, as well as a poster session preceded by lightning talks. We seek contributions of published work as well as new research, of the following, non-exclusive types:
- Analysis: what kind of situation-specific information can current models capture; what can’t they?
- Modelling: models and/or tasks aimed at integrating generic and situation-specific information in any area related to CL/NLP.
- Cross-fertilizing: how can computational models of language integrate current findings in Linguistics, Cognitive Science, and Machine Learning about generic vs. situation-specific information? How can our results inform those fields in turn?
- Position papers and surveys outlining the issues involved and ways forward.
- Submission closed as of February
7th14th 2020, 11:59PM UTC-12:00 ("anywhere on Earth")
- Notification of acceptance: March 13th 2020
- Decision of whether to postpone the symposium in relation to COVID-19: March 30th 2020
- Registration opens: To be determined, likely mid/late March. Registration is free of charge.
- GeCKo symposium: May 18th 2020