Work

Convolutional Lie Operator for Sentence Classification

Year: 2024

Type: conference-paper

Abstract: Traditional Convolutional Neural Networks have been successful in capturing local, position-invariant features in text, but their capacity to model complex transformation within language can be further explored.In this work, we explore a novel approach by integrating Lie Convolutions into Convolutional-based sentence classifiers, inspired by the ability of Lie group operations to capture complex, non-Euclidean symmetries.Our proposed models SCLie and DPCLie empirically outperform traditional Convolutional-based sentence classifiers, suggesting that Lie-based models relatively improve the accuracy by capturing transformations not commonly associated with language.Our findings motivate more exploration of new paradigms in language modeling.

Source: International Conference on Natural Language Processing and Information Retrieval (NLPIR)

DOI: 10.1145/3711542.3711600

Authors: Daniela Noemi Rim, Heeyoul Choi

Institution: Handong Global University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion

Award: G3912120501