Do Evaluation Metrics Detect Errors in Classical Chinese to English Translations?

• URL: http://arxiv.org/abs/2608.08283v2
• Date: Wed, 12 Aug 2026 12:56:06 GMT
• Title: Do Evaluation Metrics Detect Errors in Classical Chinese to English Translations?
• Abstract summary: We introduce a diagnostic framework based on minimal pairs capturing error types salient in scholarly use.<n>We find that all metrics exhibit blind spots, however MetricX24 performs best overall.
• Score: 13.334308240256364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although large language models can translate some historical languages surprisingly well, their usefulness in digital humanities workflows is limited by the lack of reliable evaluation. We investigate whether existing automatic evaluation metrics developed for modern languages are reliable in this setting, using translation from Classical Chinese to English as a test case. We introduce a diagnostic framework based on minimal pairs capturing error types salient in scholarly use, probing both reference-based and reference-free metrics for error sensitivity and tolerance to valid variation. We find that all metrics exhibit blind spots, however MetricX24 performs best overall. Our findings highlight the need for more robust and interpretable metrics for historically and culturally distinct translation settings.

Related papers

• XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics [64.77152900881724]
  We propose XQ-MEval, a semi-automatically built dataset covering nine translation directions.<n>We inject MQM-defined errors into gold translations automatically, filter them by native speakers for reliability, and merge errors to generate pseudo translations with controllable quality.<n>Using XQ-MEval, our experiments on nine representative metrics reveal the inconsistency between averaging and human judgment.
  arXiv  Detail & Related papers  (2026-04-16T12:27:10Z)
• Testing the Limits of Machine Translation from One Book [0.0]
  Current state-of-the-art models demonstrate capacity to leverage in-context learning to translate into previously unseen language contexts.<n>We focus on Kanuri, a language that, despite having substantial speaker population, has minimal digital resources.
  arXiv  Detail & Related papers  (2025-08-08T19:27:44Z)
• Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets [92.38654521870444]
  We introduce ACES, a contrastive challenge set spanning 146 language pairs. This dataset aims to discover whether metrics can identify 68 translation accuracy errors. We conduct a large-scale study by benchmarking ACES on 50 metrics submitted to the WMT 2022 and 2023 metrics shared tasks.
  arXiv  Detail & Related papers  (2024-01-29T17:17:42Z)
• A Benchmark for Evaluating Machine Translation Metrics on Dialects Without Standard Orthography [40.04973667048665]
  We evaluate how robust metrics are to non-standardized dialects. We collect a dataset of human translations and human judgments for automatic machine translations from English to two Swiss German dialects.
  arXiv  Detail & Related papers  (2023-11-28T15:12:11Z)
• xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection [21.116517555282314]
  xCOMET is an open-source learned metric designed to bridge the gap between machine translation evaluation approaches. It integrates both sentence-level evaluation and error span detection capabilities, exhibiting state-of-the-art performance across all types of evaluation. We also provide a robustness analysis with stress tests, and show that xCOMET is largely capable of identifying localized critical errors and hallucinations.
  arXiv  Detail & Related papers  (2023-10-16T15:03:14Z)
• BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training [64.37683359609308]
  In this study, we analyze various mainstream and cutting-edge automatic metrics from the perspective of their guidance for training machine translation systems. We find that certain metrics exhibit robustness defects, such as the presence of universal adversarial translations in BLEURT and BARTScore. In-depth analysis suggests two main causes of these robustness deficits: distribution biases in the training datasets, and the tendency of the metric paradigm.
  arXiv  Detail & Related papers  (2023-07-06T16:59:30Z)
• Extrinsic Evaluation of Machine Translation Metrics [78.75776477562087]
  It is unclear if automatic metrics are reliable at distinguishing good translations from bad translations at the sentence level. We evaluate the segment-level performance of the most widely used MT metrics (chrF, COMET, BERTScore, etc.) on three downstream cross-lingual tasks. Our experiments demonstrate that all metrics exhibit negligible correlation with the extrinsic evaluation of the downstream outcomes.
  arXiv  Detail & Related papers  (2022-12-20T14:39:58Z)
• On the Blind Spots of Model-Based Evaluation Metrics for Text Generation [79.01422521024834]
  We explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics. We design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics.
  arXiv  Detail & Related papers  (2022-12-20T06:24:25Z)
• DEMETR: Diagnosing Evaluation Metrics for Translation [21.25704103403547]
  We release DEMETR, a diagnostic dataset with 31K English examples. We find that learned metrics perform substantially better than string-based metrics on DEMETR.
  arXiv  Detail & Related papers  (2022-10-25T03:25:44Z)
• Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale [52.663117551150954]
  A few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models.
  arXiv  Detail & Related papers  (2020-10-26T13:57:20Z)

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.