Browse > Article
http://dx.doi.org/10.15207/JKCS.2021.12.7.037

Research on Recent Quality Estimation  

Eo, Sugyeong (Department of Computer Science and Engineering, Korea University)
Park, Chanjun (Department of Computer Science and Engineering, Korea University)
Moon, Hyeonseok (Department of Computer Science and Engineering, Korea University)
Seo, Jaehyung (Department of Computer Science and Engineering, Korea University)
Lim, Heuiseok (Department of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.12, no.7, 2021 , pp. 37-44 More about this Journal
Abstract
Quality estimation (QE) can evaluate the quality of machine translation output even for those who do not know the target language, and its high utilization highlights the need for QE. QE shared task is held every year at Conference on Machine Translation (WMT), and recently, researches applying Pretrained Language Model (PLM) are mainly being conducted. In this paper, we conduct a survey on the QE task and research trends, and we summarize the features of PLM. In addition, we used a multilingual BART model that has not yet been utilized and performed comparative analysis with the existing studies such as XLM, multilingual BERT, and XLM-RoBERTa. As a result of the experiment, we confirmed which PLM was most effective when applied to QE, and saw the possibility of applying the multilingual BART model to the QE task.
Keywords
Quality Estimation; Neural Machine Translation; Deep Learning; Language Convergence; Natural Language Processing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Vaswani et al. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
2 F. Kepler et al. (2019). Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task. arXiv preprint arXiv:1907.10352. DOI : 10.18653/v1/W19-5406   DOI
3 H. Kim, J. H. Lim, H. K. Kim & S. H. Na. (2019). QE BERT: bilingual BERT using multi-task learning for neural quality estimation. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). (pp. 85-89). DOI : 10.18653/v1/W19-5407   DOI
4 T. Ranasinghe, C. Orasan & R. Mitkov. (2020). TransQuest at WMT2020: Sentence-Level Direct Assessment. arXiv preprint arXiv:2010.05318.
5 N. Q. Luong, B. Lecouteux & L. Besacier. (2013). LIG system for WMT13 QE task: Investigating the usefulness of features in word confidence estimation for MT. In 8th Workshop on Statistical Machine Translation. (pp. 386-391).
6 C. Hardmeier, J. Nivre & J. Tiedemann. (2012). Tree kernels for machine translation quality estimation. In Seventh Workshop on Statistical Machine Translation, Montreal, Canada, June 7-8, 2012. (pp. 109-113). Association for Computational Linguistics.
7 E. Fonseca, L. Yankovskaya, A. F. Martins, M. Fishel & C. Federmann. (2019). Findings of the WMT 2019 shared tasks on quality estimation. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), 1-10. DOI : 10.18653/v1/W19-5401   DOI
8 T. Wolf et al. (2019). HuggingFace's Transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771.
9 K. Cho et al. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. DOI : 10.3115/v1/d14-1179   DOI
10 C. Park & H. Lim. (2020). A Study on the Performance Improvement of Machine Translation Using Public Korean-English Parallel Corpus. Journal of Digital Convergence, 18(6), 271-277.   DOI
11 G. Lample & A. Conneau. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291.
12 L. Specia, C. Scarton & G. H. Paetzold (2018). Quality estimation for machine translation. Synthesis Lectures on Human Language Technologies, 11(1), 1-162. DOI : 10.2200/S00854ED1V01Y201805HLT039   DOI
13 L. Specia, D. Raj & M. Turchi (2010). Machine translation evaluation versus quality estimation. Machine translation, 24(1), 39-50. DOI : 10.1007/s10590-010-9077-2   DOI
14 Y. Baek, Z. M. Kim, J. Moon, H. Kim & E. Park. (2020). Patquest: Papago translation quality estimation. In Proceedings of the Fifth Conference on Machine Translation. (pp. 991-998).
15 A. Conneau et al. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116. DOI : 10.18653/v1/P19-4007   DOI
16 Y. Liu et al. (2020). Multilingual denoising pre-training for neural machine translation. Transactions of the Association for Computational Linguistics, 8, 726-742.   DOI
17 R. Soricut, N. Bach & Z. Wang. (2012). The SDL language weaver systems in the WMT12 quality estimation shared task. In Proceedings of the Seventh Workshop on Statistical Machine Translation. (pp. 145-151).
18 S. Hochreiter & J. Schmidhuber. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. DOI : 10.1162/neco.1997.9.8.1735   DOI
19 R. N. Patel. (2016). Translation quality estimation using recurrent neural network. arXiv preprint arXiv:1610.04841. DOI : 10.18653/v1/W16-2389   DOI
20 H. Kim, J. H. Lee & S. H. Na. (2017, September). Predictor-estimator using multilevel task learning with stack propagation for neural quality estimation. In Proceedings of the Second Conference on Machine Translation. (pp. 562-568). DOI : 10.18653/v1/w17-4763   DOI
21 H. Kim & J. H. Lee. (2016). Recurrent neural network based translation quality estimation. In Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers. (pp. 787-792). DOI : 10.18653/v1/w16-2384   DOI
22 M. Wang et al. (2020, November). Hw-tsc's participation at wmt 2020 quality estimation shared task. In Proceedings of the Fifth Conference on Machine Translation. (pp. 1056-1061).
23 H. Wu et al. (2020, November). Tencent submission for WMT20 Quality Estimation Shared Task. In Proceedings of the Fifth Conference on Machine Translation. (pp. 1062-1067).
24 M. Snover, B. Dorr, R. Schwartz, L. Micciulla & J. Makhoul. (2006, August). A study of translation edit rate with targeted human annotation. In Proceedings of association for machine translation in the Americas (Vol. 200, No. 6).
25 D. Lee. (2020). Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In Proceedings of the Fifth Conference on Machine Translation. (pp. 1024-1028).
26 J. Wang, K. Fan, B. Li, F. Zhou, B. Chen, Y. Shi & L. Si. (2018). Alibaba submission for WMT18 quality estimation task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers. (pp. 809-815). DOI : 10.18653/v1/w18-6465   DOI
27 L. Specia, F. Blain, V. Logacheva, R. Astudillo & A. Martins. (2018). Findings of the wmt 2018 shared task on quality estimation. Association for Computational Linguistics. DOI : 10.18653/v1/W18-6451   DOI
28 G. Wenzek et al. (2019). Ccnet: Extracting high quality monolingual datasets from web crawl data. arXiv preprint arXiv:1911.00359.
29 T. Pires, E. Schlinger & D. Garrette. (2019). How multilingual is multilingual bert?. arXiv preprint arXiv:1906.01502. DOI : 10.18653/v1/p19-1493   DOI
30 M. Lewis et al. (2019). Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. DOI : 10.18653/v1/2020.acl-main.703   DOI
31 C. Park, Y. Yang, K. Park & H. Lim. (2020). Decoding strategies for improving low-resource machine translation. Electronics, 9(10), 1562.   DOI
32 D. Lee. (2020). Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In Proceedings of the Fifth Conference on Machine Translation. (pp. 1024-1028).
33 L. Specia, K. Shah, J. G. De Souza & T. Cohn (2013). QuEst-A translation quality estimation framework. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 79-84.
34 J. Devlin, M. W. Chang, K. Lee & K. Toutanova. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. DOI : 10.18653/v1/N19-1423
35 E. Bicici. & A. Way. (2014). Referential translation machines for predicting translation quality. Association for Computational Linguistics. DOI : 10.18653/v1/w15-3035   DOI