Beyond Scalar Scores: Reinforcement Learning for Error-Aware Quality Estimation of Machine Translation

• URL: http://arxiv.org/abs/2602.08600v1
• Date: Mon, 09 Feb 2026 12:42:41 GMT
• Title: Beyond Scalar Scores: Reinforcement Learning for Error-Aware Quality Estimation of Machine Translation
• Authors: Archchana Sindhujan, Girish A. Koushik, Shenbin Qian, Diptesh Kanojia, Constantin Orăsan,
• Abstract summary: Quality Estimation aims to assess the quality of machine translation (MT) outputs without relying on reference translations.<n>We introduce the first segment-level QE dataset for English to Malayalam, a severely resource-scarce language pair.<n>We introduce ALOPE-RL, a policy-based reinforcement learning framework that trains efficient adapters.
• Score: 10.050982803590903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quality Estimation (QE) aims to assess the quality of machine translation (MT) outputs without relying on reference translations, making it essential for real-world, large-scale MT evaluation. Large Language Models (LLMs) have shown significant promise in advancing the field of quality estimation of machine translation. However, most of the QE approaches solely rely on scalar quality scores, offering no explicit information about the translation errors that should drive these judgments. Moreover, for low-resource languages where annotated QE data is limited, existing approaches struggle to achieve reliable performance. To address these challenges, we introduce the first segment-level QE dataset for English to Malayalam, a severely resource-scarce language pair in the QE domain, comprising human-annotated Direct Assessment (DA) scores and Translation Quality Remarks (TQR), which are short, contextual, free-form annotator comments that describe translation errors. We further introduce ALOPE-RL, a policy-based reinforcement learning framework that trains efficient adapters based on policy rewards derived from DA score and TQR. Integrating error-aware rewards with ALOPE-RL, enables LLMs to reason about translation quality beyond numeric scores. Despite being trained on a small-scale QE dataset, ALOPE-RL achieves state-of-the-art performance on English to Malayalam QE using compact LLMs (<=4B parameters}) fine-tuned with LoRA and 4-bit quantization, outperforming both larger LLM-based baselines and leading encoder-based QE models. Our results demonstrate that error-aware, policy-based learning can deliver strong QE performance under limited data and compute budgets. We release our dataset, code, and trained models to support future research.

Related papers

• LLM-Based Evaluation of Low-Resource Machine Translation: A Reference-less Dialect Guided Approach with a Refined Sylheti-English Benchmark [1.3927943269211591]
  We propose a comprehensive framework that enhances Large Language Models (LLMs)-based machine translation evaluation.<n>We extend the ONUBAD dataset by incorporating Sylheti-English sentence pairs, corresponding machine translations, and Direct Assessment (DA) scores annotated by native speakers.<n>Our evaluation shows that the proposed pipeline consistently outperforms existing methods, achieving the highest gain of +0.1083 in Spearman correlation.
  arXiv  Detail & Related papers  (2025-05-18T07:24:13Z)
• Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering [68.3400058037817]
  We introduce TREQA (Translation Evaluation via Question-Answering), a framework that extrinsically evaluates translation quality.<n>We show that TREQA is competitive with and, in some cases, outperforms state-of-the-art neural and LLM-based metrics in ranking alternative paragraph-level translations.
  arXiv  Detail & Related papers  (2025-04-10T09:24:54Z)
• When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages [9.138590152838754]
  Segment-level quality estimation (QE) is a challenging cross-lingual language understanding task.<n>We comprehensively evaluate large language models (LLMs) in zero/few-shot scenarios.<n>Our results indicate that prompt-based approaches are outperformed by the encoder-based fine-tuned QE models.
  arXiv  Detail & Related papers  (2025-01-08T12:54:05Z)
• QE-EBM: Using Quality Estimators as Energy Loss for Machine Translation [5.10832476049103]
  We propose QE-EBM, a method of employing quality estimators as trainable loss networks that can directly backpropagate to the NMT model. We examine our method on several low and high resource target languages with English as the source language.
  arXiv  Detail & Related papers  (2024-10-14T07:39:33Z)
• MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators [53.91199933655421]
  Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment.<n>We introduce a universal and training-free framework, $textbfMQM-APE, based on the idea of filtering out non-impactful errors.<n>Experiments show that our approach consistently improves both the reliability and quality of error spans against GEMBA-MQM.
  arXiv  Detail & Related papers  (2024-09-22T06:43:40Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model [75.66013048128302]
  In this work, we investigate the potential of employing the QE model as the reward model to predict human preferences for feedback training. We first identify the overoptimization problem during QE-based feedback training, manifested as an increase in reward while translation quality declines. To address the problem, we adopt a simple yet effective method that uses rules to detect the incorrect translations and assigns a penalty term to the reward scores of them.
  arXiv  Detail & Related papers  (2024-01-23T16:07:43Z)
• Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation [64.5862977630713]
  This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task. We find that reference information significantly enhances the evaluation accuracy, while surprisingly, source information sometimes is counterproductive.
  arXiv  Detail & Related papers  (2024-01-12T13:23:21Z)
• The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation [93.01964988474755]
  AutoMQM is a prompting technique which asks large language models to identify and categorize errors in translations. We study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores.
  arXiv  Detail & Related papers  (2023-08-14T17:17:21Z)
• PreQuEL: Quality Estimation of Machine Translation Outputs in Advance [32.922128367314194]
  A PreQuEL system predicts how well a given sentence will be translated, without recourse to the actual translation. We develop a baseline model for the task and analyze its performance. We show that this augmentation method can improve the performance of the Quality-Estimation task as well.
  arXiv  Detail & Related papers  (2022-05-18T18:55:05Z)
• Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation [1.7188280334580195]
  We focus on the Sentence-Level QE Shared Task of the Fifth Conference on Machine Translation (WMT20) We propose an ensemble-based predictor-estimator QE model with transfer learning to overcome such QE data scarcity challenge. We achieve the best performance on the ensemble model combining the models pretrained by individual languages as well as different levels of parallel trained corpus with a Pearson's correlation of 0.298.
  arXiv  Detail & Related papers  (2021-05-17T06:02:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.