Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations

• URL: http://arxiv.org/abs/2305.10557v2
• Date: Sat, 17 Jun 2023 09:58:02 GMT
• Title: Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations
• Authors: Baikjin Jung, Myungji Lee, Jong-Hyeok Lee, Yunsu Kim
• Abstract summary: We propose a linguistically motivated method of regularization that is expected to enhance APE models' understanding of the target language. Our analysis of experimental results demonstrates that the proposed method helps improving the state-of-the-art architecture's APE quality for high-quality MTs.
• Score: 4.217162744375792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic postediting (APE) is an automated process to refine a given machine translation (MT). Recent findings present that existing APE systems are not good at handling high-quality MTs even for a language pair with abundant data resources, English-to-German: the better the given MT is, the harder it is to decide what parts to edit and how to fix these errors. One possible solution to this problem is to instill deeper knowledge about the target language into the model. Thus, we propose a linguistically motivated method of regularization that is expected to enhance APE models' understanding of the target language: a loss function that encourages symmetric self-attention on the given MT. Our analysis of experimental results demonstrates that the proposed method helps improving the state-of-the-art architecture's APE quality for high-quality MTs.

Related papers

• Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations [14.149224539732913]
  Machine Translation remains one of the last NLP tasks where large language models (LLMs) have not yet replaced dedicated supervised systems. This work exploits the complementary strengths of LLMs and supervised MT by guiding LLMs to automatically post-edit MT with external feedback on its quality. Experiments on Chinese-English, English-German, and English-Russian MQM data, we demonstrate that prompting LLMs to post-edit MT improves TER, BLEU and COMET scores. Fine-tuning helps integrate fine-grained feedback more effectively and further improves translation quality based on both automatic and human evaluation.
  arXiv  Detail & Related papers  (2024-04-11T15:47:10Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)
• 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)
• Revisiting Machine Translation for Cross-lingual Classification [91.43729067874503]
  Most research in the area focuses on the multilingual models rather than the Machine Translation component. We show that, by using a stronger MT system and mitigating the mismatch between training on original text and running inference on machine translated text, translate-test can do substantially better than previously assumed.
  arXiv  Detail & Related papers  (2023-05-23T16:56:10Z)
• Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation [12.376309678270275]
  Perturbation-based QE works simply by analyzing MT system output on perturbed input source sentences. Our approach is better at detecting gender bias and word-sense-disambiguation errors in translation than supervised QE.
  arXiv  Detail & Related papers  (2023-05-12T13:10:57Z)
• Evaluating and Improving the Coreference Capabilities of Machine Translation Models [30.60934078720647]
  Machine translation requires a wide range of linguistic capabilities. Current end-to-end models are expected to learn implicitly by observing aligned sentences in bilingual corpora.
  arXiv  Detail & Related papers  (2023-02-16T18:16:09Z)
• An Empirical Study of Automatic Post-Editing [56.86393786396992]
  APE aims to reduce manual post-editing efforts by automatically correcting errors in machine-translated output. To alleviate the lack of genuine training data, most of the current APE systems employ data augmentation methods to generate large-scale artificial corpora. We study the outputs of the state-of-art APE model on a difficult APE dataset to analyze the problems in existing APE systems.
  arXiv  Detail & Related papers  (2022-09-16T07:38:27Z)
• When Does Translation Require Context? A Data-driven, Multilingual Exploration [71.43817945875433]
  proper handling of discourse significantly contributes to the quality of machine translation (MT) Recent works in context-aware MT attempt to target a small set of discourse phenomena during evaluation. We develop the Multilingual Discourse-Aware benchmark, a series of taggers that identify and evaluate model performance on discourse phenomena.
  arXiv  Detail & Related papers  (2021-09-15T17:29:30Z)
• Unsupervised Quality Estimation for Neural Machine Translation [63.38918378182266]
  Existing approaches require large amounts of expert annotated data, computation and time for training. We devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. We achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models.
  arXiv  Detail & Related papers  (2020-05-21T12:38:06Z)

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.