Character-level Transformer-based Neural Machine Translation

Neural machine translation (NMT) is nowadays commonly applied at the subword level, using byte-pair encoding. A promising alternative approach focuses on character-level translation, which simplifies processing pipelines in NMT considerably. This approach, however, must consider relatively longer sequences, rendering the training process prohibitively expensive. In this paper, we discuss a novel, Transformer-based approach, that we compare, both in speed and in quality to the Transformer at subword and character levels, as well as previously developed character-level models. We evaluate our models on 4 language pairs from WMT'15: DE-EN, CS-EN, FI-EN and RU-EN. The proposed novel architecture can be trained on a single GPU and is 34% percent faster than the character-level Transformer; still, the obtained results are at least on par with it. In addition, our proposed model outperforms the subword-level model in FI-EN and shows close results in CS-EN. To stimulate further research in this area and close the gap with subword-level NMT, we make all our code and models publicly available

On the Feasibility of Automated Detection of Allusive Text Reuse

The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely---commonly based on none or very few shared words. Arguably, lexical semantics can be resorted to since uncovering semantic relations between words has the potential to increase the support underlying the allusion and alleviate the lexical sparsity. A further obstacle is the lack of evaluation benchmark corpora, largely due to the highly interpretative character of the annotation process. In the present paper, we aim to elucidate the feasibility of automated allusion detection. We approach the matter from an Information Retrieval perspective in which referencing texts act as queries and referenced texts as relevant documents to be retrieved, and estimate the difficulty of benchmark corpus compilation by a novel inter-annotator agreement study on query segmentation. Furthermore, we investigate to what extent the integration of lexical semantic information derived from distributional models and ontologies can aid retrieving cases of allusive reuse. The results show that (i) despite low agreement scores, using manual queries considerably improves retrieval performance with respect to a windowing approach, and that (ii) retrieval performance can be moderately boosted with distributional semantics

DHBeNeLux : incubator for digital humanities in Belgium, the Netherlands and Luxembourg

Digital Humanities BeNeLux is a grass roots initiative to foster knowledge networking and dissemination in digital humanities in Belgium, the Netherlands, and Luxembourg. This special issue highlights a selection of the work that was presented at the DHBenelux 2015 Conference by way of anthology for the digital humanities currently being done in the Benelux area and beyond. The introduction describes why this grass roots initiative came about and how DHBenelux is currently supporting community building and knowledge exchange for digital humanities in the Benelux area and how this is integrating regional digital humanities in the larger international digital humanities environment

Collaborative authorship in the twelfth century: a stylometric study of Hildegard of Bingen and Guibert of Gembloux

Abstract – Hildegard of Bingen (1098–1179) is one of the most influential female authors of the Middle Ages. From the point of view of computational stylistics, the oeuvre attributed to Hildegard is fascinating. Hildegard dictated her texts to secretaries in Latin, a language of which she did not master all grammatical subtleties. She therefore allowed her scribes to correct her spelling and grammar. Especially Hildegard’s last collaborator, Guibert of Gembloux, seems to have considerably reworked her works during his secretaryship. Whereas her other scribes were only allowed to make superficial linguistic changes, Hildegard would have permitted Guibert to render her language stylistically more elegant. In this article, we focus on two shorter texts: the Visio ad Guibertum missa and Visio de sancto Martino, both of which Hildegard allegedly authored during Guibert’s secretaryship. We analyse a corpus containing the letter collections of Hildegard, Guibert and Bernard of Clairvaux using a number of common stylometric techniques. We discuss our results in the light of the Synergy Hypothesis, suggesting that texts resulting from collaboration can display a style markedly different from that of the collaborating authors. Finally, we demonstrate that Guibert must have reworked the disputed visionary texts allegedly authored by Hildegard to such an extent that style-oriented computational procedures attribute the texts to Guibert

Assessing the stylistic properties of neurally generated text in authorship attribution

Recent applications of neural language models have led to an increased interest in the automatic generation of natural language. However impressive, the evaluation of neurally generated text has so far remained rather informal and anecdotal. Here, we present an attempt at the systematic assessment of one aspect of the quality of neurally generated text. We focus on a specific aspect of neural language generation: its ability to reproduce authorial writing styles. Using established models for authorship attribution, we empirically assess the stylistic qualities of neurally generated text. In comparison to conventional language models, neural models generate fuzzier text that is relatively harder to attribute correctly. Nevertheless, our results also suggest that neurally generated text offers more valuable perspectives for the augmentation of training data

Warren, Michelle R. 2022. Holy Digital Grail. A Medieval Book on the Internet. Stanford: Stanford University Press. Pp. xiii + 342. ISBN 9781503608009.

Book review of Warren, Michelle R. 2022. Holy Digital Grail. A Medieval Book on the Internet. Stanford: Stanford University Press. Pp. xiii + 342. ISBN 9781503608009