LangMark: A Multilingual Dataset for Automatic Post-Editing

• URL: http://arxiv.org/abs/2511.17153v1
• Date: Fri, 21 Nov 2025 11:18:15 GMT
• Title: LangMark: A Multilingual Dataset for Automatic Post-Editing
• Authors: Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin, Aaron Schliem, Dimitrios Zaikis, Roger Wechsler,
• Abstract summary: LangMark is a new human-annotated multilingual APE dataset for English translation to seven languages.<n>The dataset has 206,983 triplets, with each triplet consisting of a source segment, its NMT output, and a human post-edited translation.
• Score: 13.873007459228987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic post-editing (APE) aims to correct errors in machine-translated text, enhancing translation quality, while reducing the need for human intervention. Despite advances in neural machine translation (NMT), the development of effective APE systems has been hindered by the lack of large-scale multilingual datasets specifically tailored to NMT outputs. To address this gap, we present and release LangMark, a new human-annotated multilingual APE dataset for English translation to seven languages: Brazilian Portuguese, French, German, Italian, Japanese, Russian, and Spanish. The dataset has 206,983 triplets, with each triplet consisting of a source segment, its NMT output, and a human post-edited translation. Annotated by expert human linguists, our dataset offers both linguistic diversity and scale. Leveraging this dataset, we empirically show that Large Language Models (LLMs) with few-shot prompting can effectively perform APE, improving upon leading commercial and even proprietary machine translation systems. We believe that this new resource will facilitate the future development and evaluation of APE systems.

Related papers

• Extending Multilingual Machine Translation through Imitation Learning [60.15671816513614]
  Imit-MNMT treats the task as an imitation learning process, which mimicks the behavior of an expert. We show that our approach significantly improves the translation performance between the new and the original languages. We also demonstrate that our approach is capable of solving copy and off-target problems.
  arXiv  Detail & Related papers  (2023-11-14T21:04:03Z)
• Romanization-based Large-scale Adaptation of Multilingual Language Models [124.57923286144515]
  Large multilingual pretrained language models (mPLMs) have become the de facto state of the art for cross-lingual transfer in NLP. We study and compare a plethora of data- and parameter-efficient strategies for adapting the mPLMs to romanized and non-romanized corpora of 14 diverse low-resource languages. Our results reveal that UROMAN-based transliteration can offer strong performance for many languages, with particular gains achieved in the most challenging setups.
  arXiv  Detail & Related papers  (2023-04-18T09:58:34Z)
• Improving Multilingual Neural Machine Translation System for Indic Languages [0.0]
  We propose a multilingual neural machine translation (MNMT) system to address the issues related to low-resource language translation. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models.
  arXiv  Detail & Related papers  (2022-09-27T09:51:56Z)
• DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages [5.367993194110256]
  DivEMT is the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse set of target languages. We assess the impact on translation productivity of two state-of-the-art NMT systems, namely: Google Translate and the open-source multilingual model mBART50.
  arXiv  Detail & Related papers  (2022-05-24T17:22:52Z)
• Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning [48.15259834021655]
  We present a pragmatic approach towards building a multilingual machine translation model that covers hundreds of languages. We use a mixture of supervised and self-supervised objectives, depending on the data availability for different language pairs. We demonstrate that the synergy between these two training paradigms enables the model to produce high-quality translations in the zero-resource setting.
  arXiv  Detail & Related papers  (2022-01-09T23:36:44Z)
• Multilingual Neural Semantic Parsing for Low-Resourced Languages [1.6244541005112747]
  We introduce a new multilingual semantic parsing dataset in English, Italian and Japanese. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset. We find that a semantic trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.
  arXiv  Detail & Related papers  (2021-06-07T09:53:02Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT [129.99918589405675]
  We present an effective approach that reuses an LM that is pretrained only on the high-resource language. The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model. Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq)
  arXiv  Detail & Related papers  (2020-09-16T11:37:10Z)
• Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation [54.52971020087777]
  Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. Self-supervision improves zero-shot translation quality in multilingual models. We get up to 33 BLEU on ro-en translation without any parallel data or back-translation.
  arXiv  Detail & Related papers  (2020-05-11T00:20:33Z)

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.