KU X Upstage's submission for the WMT22 Quality Estimation: Critical Error Detection Shared Task

Sugyeong Eo, Chanjun Park, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents KU X Upstage's submission to the quality estimation (QE): critical error detection (CED) shared task in WMT22. We leverage the XLM-RoBERTa large model without utilizing any additional parallel data. To the best of our knowledge, we apply prompt-based fine-tuning to the QE task for the first time. To maximize the model's language understanding capability, we reformulate the CED task to be similar to the masked language model objective, which is a pre-training strategy of the language model. We design intuitive templates and label words, and include auxiliary descriptions such as demonstration or Google Translate results in the input sequence. We further improve the performance through the template ensemble, and as a result of the shared task, our approach achieve the best performance for both English-German and Portuguese-English language pairs in an unconstrained setting.

Original languageEnglish
Title of host publicationWMT 2022 - 7th Conference on Machine Translation, Proceedings of the Conference
PublisherAssociation for Computational Linguistics
Pages606-614
Number of pages9
ISBN (Electronic)9781959429296
Publication statusPublished - 2022
Event7th Conference on Machine Translation, WMT 2022 - Abu Dhabi, United Arab Emirates
Duration: 2022 Dec 72022 Dec 8

Publication series

NameConference on Machine Translation - Proceedings
ISSN (Electronic)2768-0983

Conference

Conference7th Conference on Machine Translation, WMT 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period22/12/722/12/8

Bibliographical note

Funding Information:
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2022-2018-0-01405) supervised by the Institute for Information & Communications Technology Planning & Evaluation(IITP) and this work was supported by an IITP grant funded by the MSIT (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques) and this research was supported by the MSIT, Korea, under the ICT Creative Consilience program (IITP-2022-2020-0-01819) supervised by the IITP.

Publisher Copyright:
© 2022 Association for Computational Linguistics.

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Software

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