Handedness —the use of one versus two hands in sign production— has traditionally been discussed in relation to dominance and symmetry conditions, yet it remains underrepresented in formal phonological models of sign languages. This paper argues that handedness constitutes a core phonological parameter that directly influences the structure and interaction of movement, handshape, location, and orientation. Building on hierarchical and dependency-based approaches, we propose an adapted phonological dependency model that explicitly integrates handedness in the representation of manual articulators. In one-handed signs, features are specified for a single active hand. In two-handed signs, feature distribution is constrained by symmetry and dominance conditions, which regulate whether the hands must share features or may differ in a structurally restricted way. This structural encoding accounts for variation phenomena such as weak add, weak prop, and weak drop as constrained adjustments within the phonological system. From a technical perspective, this refinement suggests more formal restrictiveness and empirical discriminability within the feature geometries, reduced representational ambiguity, and improved empirical testability across theoretical, corpus-based, and computational implementations, strengthening the interface between phonological theory and sign language technology.

Despite the rapid advances in AI and its impact on machine translation (MT), when it comes to sign language (SL) processing and MT, there is a big bottleneck – the lack of substantial quantities of quality signed data suitable for developing SLMT models. Marker-based motion capturing (MoCap) is a technique for tracing and recording the body movements (including hands and figures) in 3D space with high precision and has been widely used in SL research. MoCap data is of high representative accuracy, making it very suitable for analysing movement patterns and articulatory features. However, it is also very complex – a recording of a single sign may contain more than 240 entries over 156 features making it difficult for processing. In this paper we analyse MoCap data aiming to understand which captured features are of high importance. Consecutively, we optimise the MoCap data representation, reducing the number of features, and assess how this feature- reduced data impacts sign classification task. We organise MoCap features based on their importance and show how models trained on feature-reduced representations outperform those developed on the complete feature set.

Machine translation (MT) has evolved rapidly over the last 70 years thanks to the advances in processing technology, methodologies as well as the ever-increasing volumes of data. This trend is observed in the context of MT for spoken languages. However, when it comes to sign languages (SL) translation technologies, the progress is much slower; SLMT is still in its infancy with limited applications. One of the main factors for this set back is the lack of effective, respectful and fair user involvement across the different phases of the research and development of SLMT. We present a meta-review of 111 articles on SLMT from the perspective of user involvement. Our analysis investigates what users are involved and what tasks they assume in the first four phrases of MT research: (i) Problem and definition, (ii) Dataset construction, (iii) Model Design and Training, (iv) Model Validation and Evaluation. We find out that users have primarily been involved as data creators and monitors as well as evaluators. We assess that effective co-creation, as defined in (Lepp et al., 2025), has not been performed and conclude with recommendations for improving the MT research and development landscape from a co-creative perspective.