ICL’s Submission to the WMT21 Critical Error Detection Shared Task

Genze Jiang, Zhenhao Li, Lucia Specia


Abstract
This paper presents Imperial College London’s submissions to the WMT21 Quality Estimation (QE) Shared Task 3: Critical Error Detection. Our approach builds on cross-lingual pre-trained representations in a sequence classification model. We further improve the base classifier by (i) adding a weighted sampler to deal with unbalanced data and (ii) introducing feature engineering, where features related to toxicity, named-entities and sentiment, which are potentially indicative of critical errors, are extracted using existing tools and integrated to the model in different ways. We train models with one type of feature at a time and ensemble those models that improve over the base classifier on the development (dev) set. Our official submissions achieve very competitive results, ranking second for three out of four language pairs.
Anthology ID:
2021.wmt-1.97
Volume:
Proceedings of the Sixth Conference on Machine Translation
Month:
November
Year:
2021
Address:
Online
Venues:
EMNLP | WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
928–934
Language:
URL:
https://aclanthology.org/2021.wmt-1.97
DOI:
Bibkey:
Cite (ACL):
Genze Jiang, Zhenhao Li, and Lucia Specia. 2021. ICL’s Submission to the WMT21 Critical Error Detection Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 928–934, Online. Association for Computational Linguistics.
Cite (Informal):
ICL’s Submission to the WMT21 Critical Error Detection Shared Task (Jiang et al., WMT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wmt-1.97.pdf