Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling
This provides a practical toolkit for researchers and practitioners working with temporal language data, though it is incremental as it builds on existing signature-based methods.
The authors introduced Sig-Networks, an open-source toolkit for longitudinal language modeling that incorporates signature-based neural network models, achieving state-of-the-art performance on three NLP tasks including counseling conversations, rumour stance switch, and mood changes in social media threads.
We present an open-source, pip installable toolkit, Sig-Networks, the first of its kind for longitudinal language modelling. A central focus is the incorporation of Signature-based Neural Network models, which have recently shown success in temporal tasks. We apply and extend published research providing a full suite of signature-based models. Their components can be used as PyTorch building blocks in future architectures. Sig-Networks enables task-agnostic dataset plug-in, seamless pre-processing for sequential data, parameter flexibility, automated tuning across a range of models. We examine signature networks under three different NLP tasks of varying temporal granularity: counselling conversations, rumour stance switch and mood changes in social media threads, showing SOTA performance in all three, and provide guidance for future tasks. We release the Toolkit as a PyTorch package with an introductory video, Git repositories for preprocessing and modelling including sample notebooks on the modeled NLP tasks.