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.- Anthology ID:
- 2022.acl-short.69
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 620–627
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.69
- DOI:
- 10.18653/v1/2022.acl-short.69
- Cite (ACL):
- Shivam Agarwal, Ramit Sawhney, Sanchit Ahuja, Ritesh Soun, and Sudheer Chava. 2022. HYPHEN: Hyperbolic Hawkes Attention For Text Streams. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 620–627, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- HYPHEN: Hyperbolic Hawkes Attention For Text Streams (Agarwal et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.acl-short.69.pdf
- Code
- gtfintechlab/hyphen-acl
- Data
- StockNet