Automatic Post-Editing for Translating Chinese Novels to Vietnamese

• URL: http://arxiv.org/abs/2104.12128v1
• Date: Sun, 25 Apr 2021 10:59:31 GMT
• Title: Automatic Post-Editing for Translating Chinese Novels to Vietnamese
• Authors: Thanh Vu and Dai Quoc Nguyen
• Abstract summary: We construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task.
• Score: 13.018476689878637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present the first attempt to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.

Related papers

• 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)
• Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques [3.257358540764261]
  We present a novel approach of using a scaled similarity score of sentences, especially for related languages based on a 5-gram KenLM language model. Our approach succeeds in increasing 2 BLEU point on multi-domain approach, 3 BLEU point on fine-tuning for NMT and 2 BLEU point on iterative back-translation approach.
  arXiv  Detail & Related papers  (2023-03-03T09:07:30Z)
• 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)
• PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation [6.950742601378329]
  We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs. This is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. In both automatic and human evaluations, the best performance is obtained by fine-tuning the pre-trained sequence-to-sequence denoising auto-encoder mBART.
  arXiv  Detail & Related papers  (2021-10-23T11:42:01Z)
• VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization? [1.1379578593538398]
  We investigate the robustness of transformer-based encoder-decoder architectures for Vietnamese abstractive summarization. We validate the performance of the methods on two Vietnamese datasets.
  arXiv  Detail & Related papers  (2021-10-08T17:10:31Z)
• Non-Parametric Online Learning from Human Feedback for Neural Machine Translation [54.96594148572804]
  We study the problem of online learning with human feedback in the human-in-the-loop machine translation. Previous methods require online model updating or additional translation memory networks to achieve high-quality performance. We propose a novel non-parametric online learning method without changing the model structure.
  arXiv  Detail & Related papers  (2021-09-23T04:26:15Z)
• Alternated Training with Synthetic and Authentic Data for Neural Machine Translation [49.35605028467887]
  We propose alternated training with synthetic and authentic data for neural machine translation (NMT) Compared with previous work, we introduce authentic data as guidance to prevent the training of NMT models from being disturbed by noisy synthetic data. Experiments on Chinese-English and German-English translation tasks show that our approach improves the performance over several strong baselines.
  arXiv  Detail & Related papers  (2021-06-16T07:13:16Z)
• Exploring Unsupervised Pretraining Objectives for Machine Translation [99.5441395624651]
  Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT) Most approaches adapt masked-language modeling (MLM) to sequence-to-sequence architectures, by masking parts of the input and reconstructing them in the decoder. We compare masking with alternative objectives that produce inputs resembling real (full) sentences, by reordering and replacing words based on their context.
  arXiv  Detail & Related papers  (2021-06-10T10:18:23Z)
• Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings [51.47607125262885]
  We describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. We validate our technique by extracting parallel sentence pairs on the BUCC 2017 bitext mining task and observe up to a 24.5 point increase (absolute) in F1 scores over previous unsupervised methods.
  arXiv  Detail & Related papers  (2020-10-15T14:04:03Z)
• SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task [111.91077204077817]
  We participated in four translation directions of three language pairs: English-Chinese, English-Polish, and German-Upper Sorbian. Based on different conditions of language pairs, we have experimented with diverse neural machine translation (NMT) techniques. In our submissions, the primary systems won the first place on English to Chinese, Polish to English, and German to Upper Sorbian translation directions.
  arXiv  Detail & Related papers  (2020-10-11T00:40:05Z)
• Can Automatic Post-Editing Improve NMT? [9.233407096706744]
  Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort. APE has had notable success when used with statistical machine translation (SMT) systems but has not been as successful over neural machine translation (NMT) systems.
  arXiv  Detail & Related papers  (2020-09-30T02:34:19Z)

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.