In this paper, we reproduce some of the experiments related to neural network training for Machine Translation as reported in (Vanmassenhove and Way, 2018). They annotated a sample from the EN-FR and EN-DE Europarl aligned corpora with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation (NMT) toolkit. Following the original publication, we obtained lower BLEU scores than the authors of the original paper, but on a more limited set of annotations. In the second half of the paper, we try to analyze the difference in the results obtained and suggest some methods to improve the results. We discuss the Byte Pair Encoding (BPE) used in the pre-processing phase and suggest feature ablation in relation to the granularity of syntactic and semantic annotations. The learnability of the annotated input is discussed in relation to existing resources for the target languages. We also discuss the feature representation likely to have been adopted for combining features.

This paper presents an annotation framework for nasal grunts of the whole French CID corpus (Bertrand et al., 2008). The acoustic components under scrutiny are justified and the annotation guidelines are described. We carefully characterise the acoustic cues and visual cues followed by the annotator, especially for non-modal phonation types. The conventions followed for the annotation of interactional and positional properties of grunts are explained. The resulting datasets after data extraction with Praat scripts (Boersma and Weenink, 2019) are analysed with R (R Core Team, 2017), focusing on duration. We analyse the effect of non-modal phonation (especially ingressive phonation) on duration and discuss a specialisation of grunts observed in the CID for grunts with ingressive phonation. The more general aim of this research is to establish putative core and additive properties of grunts and a tentative typology of grunts in spoken interactions.

This paper describes the workflow and architecture adopted by a linguistic research project. We report our experience and present the research outputs turned into resources that we wish to share with the community. We discuss the current limitations and the next steps that could be taken for the scaling and development of our research project. Allying NLP and language-centric AI, we discuss similar projects and possible ways to start collaborating towards potential platform interoperability.