Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement

• URL: http://arxiv.org/abs/2209.05695v1
• Date: Tue, 13 Sep 2022 02:37:12 GMT
• Title: Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement
• Authors: Zhen Yang, Fandong Meng, Yuanmeng Yan and Jie Zhou
• Abstract summary: We create a benchmark dataset, emphHJQE, where the expert translators directly annotate poorly translated words. We propose two tag correcting strategies, namely tag refinement strategy and tree-based annotation strategy, to make the TER-based artificial QE corpus closer to emphHJQE. The results show our proposed dataset is more consistent with human judgement and also confirm the effectiveness of the proposed tag correcting strategies.
• Score: 57.72846454929923
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Word-level Quality Estimation (QE) of Machine Translation (MT) aims to find out potential translation errors in the translated sentence without reference. Typically, conventional works on word-level QE are designed to predict the translation quality in terms of the post-editing effort, where the word labels ("OK" and "BAD") are automatically generated by comparing words between MT sentences and the post-edited sentences through a Translation Error Rate (TER) toolkit. While the post-editing effort can be used to measure the translation quality to some extent, we find it usually conflicts with the human judgement on whether the word is well or poorly translated. To overcome the limitation, we first create a golden benchmark dataset, namely \emph{HJQE} (Human Judgement on Quality Estimation), where the expert translators directly annotate the poorly translated words on their judgements. Additionally, to further make use of the parallel corpus, we propose the self-supervised pre-training with two tag correcting strategies, namely tag refinement strategy and tree-based annotation strategy, to make the TER-based artificial QE corpus closer to \emph{HJQE}. We conduct substantial experiments based on the publicly available WMT En-De and En-Zh corpora. The results not only show our proposed dataset is more consistent with human judgment but also confirm the effectiveness of the proposed tag correcting strategies.\footnote{The data can be found at \url{https://github.com/ZhenYangIACAS/HJQE}.}

Related papers

• 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. We introduce a universal and training-free framework, $textbfMQM-APE, to enhance the quality of error annotations predicted by LLM evaluators.
  arXiv  Detail & Related papers  (2024-09-22T06:43:40Z)
• A Data Selection Approach for Enhancing Low Resource Machine Translation Using Cross-Lingual Sentence Representations [0.4499833362998489]
  This study focuses on the case of English-Marathi language pairs, where existing datasets are notably noisy. To mitigate the impact of data quality issues, we propose a data filtering approach based on cross-lingual sentence representations. Results demonstrate a significant improvement in translation quality over the baseline post-filtering with IndicSBERT.
  arXiv  Detail & Related papers  (2024-09-04T13:49:45Z)
• Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective [72.83966378613238]
  Under-translation and over-translation remain two challenging problems in state-of-the-art Neural Machine Translation (NMT) systems. We conduct an in-depth analysis on the underlying cause of under-translation in NMT, providing an explanation from the perspective of decoding objective. We propose employing the confidence of predicting End Of Sentence (EOS) as a detector for under-translation, and strengthening the confidence-based penalty to penalize candidates with a high risk of under-translation.
  arXiv  Detail & Related papers  (2024-05-29T09:25:49Z)
• Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task [59.46906545506715]
  We introduce the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks. Our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks.
  arXiv  Detail & Related papers  (2023-09-23T01:52:14Z)
• 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)
• Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? [61.866103154161884]
  Neural machine translation (NMT) is often criticized for failures that happen without awareness. We propose a novel competency-aware NMT by extending conventional NMT with a self-estimator. We show that the proposed method delivers outstanding performance on quality estimation.
  arXiv  Detail & Related papers  (2022-11-25T02:39:41Z)
• Mismatching-Aware Unsupervised Translation Quality Estimation For Low-Resource Languages [6.049660810617423]
  XLMRScore is a cross-lingual counterpart of BERTScore computed via the XLM-RoBERTa (XLMR) model. We evaluate the proposed method on four low-resource language pairs of the WMT21 QE shared task.
  arXiv  Detail & Related papers  (2022-07-31T16:23:23Z)
• HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation [0.0]
  In this work, we introduce HOPE, a task-oriented and human-centric evaluation framework for machine translation output. It contains only a limited number of commonly occurring error types, and use a scoring model with geometric progression of error penalty points (EPPs) reflecting error severity level to each translation unit. The approach has several key advantages, such as ability to measure and compare less than perfect MT output from different systems, ability to indicate human perception of quality, immediate estimation of the labor effort required to bring MT output to premium quality, low-cost and faster application, as well as higher IRR.
  arXiv  Detail & Related papers  (2021-12-27T18:47:43Z)

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.