Sign Language Gibberish for syntactic parsing evaluation

TL;DR

This paper introduces a method to generate synthetic sign language data for evaluating syntactic parsers, addressing the scarcity of real annotated data and enabling scalability testing of parsing techniques.

Contribution

It presents a novel data generation approach for sign language syntax evaluation, facilitating parser assessment without relying on limited real datasets.

Findings

Synthetic datasets help evaluate parser scalability

The approach models dependency grammars for sign language

Enables large-scale parser testing

Abstract

Sign Language (SL) automatic processing slowly progresses bottom-up. The field has seen proposition to handle the video signal, to recognize and synthesize sublexical and lexical units. It starts to see the development of supra-lexical processing. But the recognition, at this level, lacks data. The syntax of SL appears very specific as it uses massively the multiplicity of articulators and its access to the spatial dimensions. Therefore new parsing techniques are developed. However these need to be evaluated. The shortage on real data restrains the corpus-based models to small sizes. We propose here a solution to produce data-sets for the evaluation of parsers on the specific properties of SL. The article first describes the general model used to generates dependency grammars and the phrase generation from these lasts. It then discusses the limits of approach. The solution shows to be of particular interest to evaluate the scalability of the techniques on big models.