Work

Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting

Year: 2024

Type: conference-paper

Abstract: In machine learning, ensembles are important tools for improving the model performance. In natural language processing specifically, ensembles boost the performance of a method due to multiple large models available in open source. However, existing approaches mostly rely on simple averaging of predictions by ensembles with equal weights for each model, ignoring differences in the quality and conformity of models. We propose to estimate weights for ensembles of NLP models using not only knowledge of their individual performance but also their similarity to each other. By adopting distance measures based on Topological Data Analysis (TDA), we improve our ensemble. The quality improves for both text classification accuracy and relevant uncertainty estimation.

Source: IEEE International Conference on Data Science and Advanced Analytics (DSAA)

DOI: 10.1109/dsaa61799.2024.10722805

Authors: Polina Proskura, Alexey Zaytsev

Institutions: Institute for Information Transmission Problems, Skolkovo Institute of Science and Technology

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Russian Science Foundation

Award: G2805551362