DIALOG-22 RuATD Generated Text Detection

Abstract

Text Generation Models (TGMs) succeed in creating text that matches human language style reasonably well. Detectors that can distinguish between TGM-generated text and human-written ones play an important role in preventing abuse of TGM. In this paper, we describe our pipeline for the two DIALOG-22 RuATD tasks: detecting generated text (binary task) and classification of which model was used to generate text (multiclass task). We achieved 1st place on the binary classification task with an accuracy score of 0.82995 on the private test set and 4th place on the multiclass classification task with an accuracy score of 0.62856 on the private test set. We proposed an ensemble method of different pre-trained models based on the attention mechanism.

6 pages

References in corpus (4)

• Multilingual Denoising Pre-training for Neural Machine Translation
• DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
• CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
• Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian