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Main Authors: Zimin, Mikhail, Shamsutdinova, Milyausha, Andriushchenko, Georgii
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2512.06586
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author Zimin, Mikhail
Shamsutdinova, Milyausha
Andriushchenko, Georgii
author_facet Zimin, Mikhail
Shamsutdinova, Milyausha
Andriushchenko, Georgii
contents Ensuring factual consistency in generated text is crucial for reliable natural language processing applications. However, there is a lack of evaluation tools for factual consistency in Russian texts, as existing tools primarily focus on English corpora. To bridge this gap, we introduce AlignRuScore, a comprehensive adaptation of the AlignScore metric for Russian. To adapt the metric, we fine-tuned a RuBERT-based alignment model with task-specific classification and regression heads on Russian and translated English datasets. Our results demonstrate that a unified alignment metric can be successfully ported to Russian, laying the groundwork for robust multilingual factual consistency evaluation. We release the translated corpora, model checkpoints, and code to support further research.
format Preprint
id arxiv_https___arxiv_org_abs_2512_06586
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Adapting AlignScore Mertic for Factual Consistency Evaluation of Text in Russian: A Student Abstract
Zimin, Mikhail
Shamsutdinova, Milyausha
Andriushchenko, Georgii
Computation and Language
Ensuring factual consistency in generated text is crucial for reliable natural language processing applications. However, there is a lack of evaluation tools for factual consistency in Russian texts, as existing tools primarily focus on English corpora. To bridge this gap, we introduce AlignRuScore, a comprehensive adaptation of the AlignScore metric for Russian. To adapt the metric, we fine-tuned a RuBERT-based alignment model with task-specific classification and regression heads on Russian and translated English datasets. Our results demonstrate that a unified alignment metric can be successfully ported to Russian, laying the groundwork for robust multilingual factual consistency evaluation. We release the translated corpora, model checkpoints, and code to support further research.
title Adapting AlignScore Mertic for Factual Consistency Evaluation of Text in Russian: A Student Abstract
topic Computation and Language
url https://arxiv.org/abs/2512.06586