The First NLPL Workshop on Deep Learning for Natural Language Processing will be held on September 30, 2019 in Turku, Finland (co-located with the NoDaLiDa Conference).
Topic and Goals
The use of deep neural networks and related techniques has led to major improvements on many NLP tasks and has in a short time profoundly changed the research landscape in our field. Making adequate use of these techniques, however, not only requires new technical knowledge on the part of researchers but also presupposes access to large-scale computing resources often with dedicated, specialized hardware. Keeping up with the development is therefore a challenge, especially for smaller research groups in academic environments, which are common in the Nordic countries.
The Nordic Language Processing Laboratory (NLPL) is a collaboration of university research groups in the Nordic countries, with support from the Nordic e-Infrastructure Collaboration (NeIC). The goal of NLPL is to create a virtual laboratory for data- and compute-intensive NLP research based on a common software, data and service stack in multiple Nordic HPC centers, to pool competencies within the user community and among expert support teams, and thereby to enable internationally competitive, data-intensive research and experimentation on a scale that would be difficult to sustain for individual research groups on commodity computing resources.
The First NLPL Workshop on Deep Learning for Natural Language Processing seeks to facilitate platform building and knowledge exchange among NLP researchers in the Nordic countries. The goal of the workshop is to support NLP research based on deep learning or other techniques that require high-performance computing and to provide a forum for researchers in the Nordic countries to exchange ideas and discuss ongoing research in the area. A special focus of the workshop will be how to enable competitive research given the limitations of available resources and how to ease entry into the field for new researchers. Consequently, the workshop will not be limited solely to methodological and resource description papers, but will welcome all kinds of contributions addressing the abovementioned focus.
We invite papers on all relevant topics, including but not limited to:
- Using deep learning to solve NLP problems
- Making deep learning more usable and accessible for NLP researchers
- Improving our understanding of deep learning models in the context of NLP
- Representation learning for natural language
- Interpretation of neural representations
- Intrinsic and extrinsic evaluation of deep learning models
- Contextualized and multimodal representations
- Multitask learning with deep neural networks
- Deep learning with scarce resources and noisy data
- Transfer learning and multilinguality with deep models
We invite papers ... Please see the detailed instructions for authors for additional information and available template files.