Abstract
This paper presents our contributions to the WMT2023 shared metrics task, consisting of two distinct evaluation approaches: a) Unsupervised Metric (MEE4) and b) Supervised Metric (XLSim). MEE4 represents an unsupervised, reference-based assessment metric that quantifies linguistic features, encompassing lexical, syntactic, semantic, morphological, and contextual similarities, leveraging embeddings. In contrast, XLsim is a supervised reference-based evaluation metric, employing a Siamese Architecture, which regresses on Direct Assessments (DA) from previous WMT News Translation shared tasks from 2017-2022. XLsim is trained using XLM-RoBERTa (base) on English-German reference and mt pairs with human scores.- Anthology ID:
- 2023.wmt-1.66
- Volume:
- Proceedings of the Eighth Conference on Machine Translation
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 800–805
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.66
- DOI:
- 10.18653/v1/2023.wmt-1.66
- Cite (ACL):
- Ananya Mukherjee and Manish Shrivastava. 2023. MEE4 and XLsim : IIIT HYD’s Submissions’ for WMT23 Metrics Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 800–805, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- MEE4 and XLsim : IIIT HYD’s Submissions’ for WMT23 Metrics Shared Task (Mukherjee & Shrivastava, WMT 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.wmt-1.66.pdf