Experimental Standards for Deep Learning Research: A Natural Language Processing Perspective

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, as with other fields employing DL techniques, there has been a lack of common experimental standards compared to more established disciplines. Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in DL into a single, widely-applicable methodology. Following these best practices is crucial to strengthening experimental evidence, improve reproducibility and enable scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.

The Danish Gigaword Project

Danish is a North Germanic/Scandinavian language spoken primarily in Denmark, a country with a tradition of technological and scientific innovation. However, from a technological perspective, the Danish language has received relatively little attention and, as a result, Danish language technology is hard to develop, in part due to a lack of large or broad-coverage Danish corpora. This paper describes the Danish Gigaword project, which aims to construct a freely-available one billion word corpus of Danish text that represents the breadth of the written language.

UniParse: A universal graph-based parsing toolkit

This paper describes the design and use of the graph-based parsing framework and toolkit UniParse, released as an open-source python software package. UniParse as a framework novelly streamlines research prototyping, development and evaluation of graph-based dependency parsing architectures. UniParse does this by enabling highly efficient, sufficiently independent, easily readable, and easily extensible implementations for all dependency parser components. We distribute the toolkit with ready-made configurations as re-implementations of all current state-of-the-art first-order graph-based parsers, including even more efficient Cython implementations of both encoders and decoders, as well as the required specialised loss functions.