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HYPHEN: Hyperbolic Hawkes Attention For Text Streams

2022-05-01ACL 2022Code Available1· sign in to hype

Shivam Agarwal, Ramit Sawhney, Sanchit Ahuja, Ritesh Soun, Sudheer Chava

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Abstract

Analyzing the temporal sequence of texts from sources such as social media, news, and parliamentary debates is a challenging problem as it exhibits time-varying scale-free properties and fine-grained timing irregularities. We propose a Hyperbolic Hawkes Attention Network (HYPHEN), which learns a data-driven hyperbolic space and models irregular powerlaw excitations using a hyperbolic Hawkes process. Through quantitative and exploratory experiments over financial NLP, suicide ideation detection, and political debate analysis we demonstrate HYPHEN’s practical applicability for modeling online text sequences in a geometry agnostic manner.

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