In 2018 the DGS-Korpus project published the first full release of the Public DGS Corpus. The data have already been published in two different ways to fulfil the needs of different user groups, and we have now published the third portal MY DGS – ANNIS using the ANNIS browser-based corpus software. ANNIS is a corpus query tool for visualization and querying of multi-layer corpus data. It has its own query language, AQL, and is accessed from a web browser without requiring a login. It allows more complex queries and visualizations than those provided by the existing research portal. We introduce ANNIS and its query language AQL, describe the structure of MY DGS – ANNIS, and give some example queries. The use cases with queries over multiple annotation tiers and metadata illustrate the research potential of this powerful tool and show how students and researchers can explore the Public DGS Corpus.

In this paper we survey the state of the art for the anonymisation of sign language corpora. We begin by exploring the motivations behind anonymisation and the close connection with the issue of ethics and informed consent for corpus participants. We detail how the the names which should be anonymised can be identified. We then describe the processes which can be used to anonymise both the video and the annotations belonging to a corpus, and the variety of ways in which these can be carried out. We provide examples for all of these processes from three sign language corpora in which anonymisation of the data has been performed.

While classic NLG systems typically made use of hierarchically structured content plans that included discourse relations as central components, more recent neural approaches have mostly mapped simple, flat inputs to texts without representing discourse relations explicitly. In this paper, we investigate whether it is beneficial to include discourse relations in the input to neural data-to-text generators for texts where discourse relations play an important role. To do so, we reimplement the sentence planning and realization components of a classic NLG system, Methodius, using LSTM sequence-to-sequence (seq2seq) models. We find that although seq2seq models can learn to generate fluent and grammatical texts remarkably well with sufficiently representative Methodius training data, they cannot learn to correctly express Methodius’s similarity and contrast comparisons unless the corresponding RST relations are included in the inputs. Additionally, we experiment with using self-training and reverse model reranking to better handle train/test data mismatches, and find that while these methods help reduce content errors, it remains essential to include discourse relations in the input to obtain optimal performance.

Using the Methodius Natural Language Generation (NLG) System, we have created a corpus which consists of a collection of generated texts which describe ancient Greek artefacts. Each text is linked to two representations created as part of the NLG process. The first is a content plan, which uses rhetorical relations to describe the high-level discourse structure of the text, and the second is a logical form describing the syntactic structure, which is sent to the OpenCCG surface realization module to produce the final text output. In recent work, White and Howcroft (2015) have used the SPaRKy restaurant corpus, which contains similar combination of texts and representations, for their research on the induction of rules for the combination of clauses. In the first instance this corpus will be used to test their algorithms on an additional domain, and extend their work to include the learning of referring expression generation rules. As far as we know, the SPaRKy restaurant corpus is the only existing corpus of this type, and we hope that the creation of this new corpus in a different domain will provide a useful resource to the Natural Language Generation community.