ILLC-UvA Adaptation System (Scorpio) at WMT'16 IT-DOMAIN Task

Cuong, Hoang Manh; Frank, Stella; Sima’an, Khalil

doi:10.18653/v1/w16-2330

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pahari et al (2016) describe JU-USAAR (Jadavpur University & Saarland University). Cuong et al (2016) describe ILLC-UVA-SCORPIO (University of Amsterdam). IILC-UVA-DS is based on Hoang and Sima'an (2014).…”

Section: Submitted Systemsmentioning

confidence: 99%

Findings of the 2016 Conference on Machine Translation

Bojar¹,

Chatterjee²,

Federmann³

et al. 2016

Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

420

358

View full text Add to dashboard Cite

Findings of the 2016 Conference on Machine Translation (WMT16) Bojar, O.; Chatterjee, R.; Federmann, C.; Graham, Y.; Haddow, B.; Huck, M.; Jimeno Yepes, A.; Koehn, P.; Logacheva, V.; Monz, C.; Negri, M.; Névéol, A.; Neves, M.; Popel, M.; Post, M.; Rubino, R.; Scarton, C.; Specia, L.; Turchi, M.; Verspoor, K.; Zampieri, M.Abstract This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments).The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.1 http://statmt.org/wmt16/results.html 2

show abstract

Section: Submitted Systemsmentioning

confidence: 99%

Findings of the 2016 Conference on Machine Translation

Bojar¹,

Chatterjee²,

Federmann³

et al. 2016

Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

420

358

View full text Add to dashboard Cite

show abstract

Section: Submitted Systemsmentioning

confidence: 99%

Machine translation and legal terminology

Killman

2023

Handbook of Terminology

View full text Add to dashboard Cite

This chapter addresses machine translation (MT) with an eye to legal terminology. The translation of legal terms and phrasemes may be fraught with contextual complexities, and context has long been the Achilles’ heel of MT. Nevertheless, neural MT (NMT) and statistical MT (SMT) have made considerable progress in recent years, thanks to data-driven approaches making use of potentially related corpora to overcome contextual obstacles. Such approaches and the potential frozenness or repetitiveness of legal terms and phrases may allow MT to overcome some of these obstacles. This chapter reviews contextual complexities surrounding legal terminology, NMT and SMT architectures, and research on MT and legal translation to determine what might be expected from data-driven MT in this context.

show abstract

“…Chatterjee et al, 2015) and MT for the IT domain (e.g. Cuong et al, 2016). We take this as evidence that the community as a whole is well aware of the fact that when evaluating MT quality, the actual use-case and utility of the translations therein need to be borne in mind.…”

Section: Iii1 How Is Mt Quality Measured?mentioning

confidence: 99%

Machine translation: Where are we at today?

Way¹

2020

The Bloomsbury Companion to Language Industry Studies

View full text Add to dashboard Cite

ILLC-UvA Adaptation System (Scorpio) at WMT'16 IT-DOMAIN Task

Cited by 5 publications

References 12 publications

Findings of the 2016 Conference on Machine Translation

Findings of the 2016 Conference on Machine Translation

Machine translation and legal terminology

Machine translation: Where are we at today?

Contact Info

Product

Resources

About