Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling

• URL: http://arxiv.org/abs/2312.03523v2
• Date: Tue, 6 Feb 2024 12:14:19 GMT
• Title: Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling
• Authors: Talia Tseriotou, Ryan Sze-Yin Chan, Adam Tsakalidis, Iman Munire Bilal, Elena Kochkina, Terry Lyons, Maria Liakata
• Abstract summary: We present an open-source, pip installable toolkit, Sig-Networks, 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 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.
• Score: 14.619019557308807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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