Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling
Conference of the European Chapter of the Association for Computational Linguistics(2023)
摘要
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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