Sig-Networks toolkit: signature networks for longitudinal language modelling

Internet publication

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.

Authors: Tseriotou, T; Chan, RS-Y; Tsakalidis, A; Bilal, IM; Kochkina, E

• Publication status: Published
• Peer review status: Not peer reviewed
• Host title: arXiv
• Publication date: 2023-12-06
• DOI: 10.48550/arXiv.2312.03523
• Language: English
• Keywords: computation and language