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2022 ACL ACL 2022

HYPHEN: Hyperbolic Hawkes Attention For Text Streams

Abstract

AbstractAnalyzing 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.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — hyperbolic hawkes process
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Optimization & Theory > Stochastic Processes Deep Learning > Models > Transformers Machine Learning > Learning Types > Sequence Modeling Natural Language Processing > Applications > Text Processing

Keywords

temporal modeling temporal sequence attention mechanism hyperbolic space hawkes process text stream hyperbolic hawkes process
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