ACL 2022short4 citations
HYPHEN: Hyperbolic Hawkes Attention For Text Streams
Shivam Agarwal, Ramit Sawhney, Sanchit Ahuja, Ritesh Soun, Sudheer Chava
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.
BibTeX
@inproceedings{agarwal-etal-2022-hyphen,
title = "{HYPHEN}: Hyperbolic {H}awkes Attention For Text Streams",
author = "Agarwal, Shivam and
Sawhney, Ramit and
Ahuja, Sanchit and
Soun, Ritesh and
Chava, Sudheer",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.69/",
doi = "10.18653/v1/2022.acl-short.69",
pages = "620--627"
}