@inproceedings{mukherjee-shrivastava-2023-mee4,
title = "{MEE}4 and {XL}sim : {IIIT} {HYD}{'}s Submissions{'} for {WMT}23 Metrics Shared Task",
author = "Mukherjee, Ananya and
Shrivastava, Manish",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.66",
doi = "10.18653/v1/2023.wmt-1.66",
pages = "800--805",
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.",
}
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%0 Conference Proceedings
%T MEE4 and XLsim : IIIT HYD’s Submissions’ for WMT23 Metrics Shared Task
%A Mukherjee, Ananya
%A Shrivastava, Manish
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F mukherjee-shrivastava-2023-mee4
%X 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.
%R 10.18653/v1/2023.wmt-1.66
%U https://aclanthology.org/2023.wmt-1.66
%U https://doi.org/10.18653/v1/2023.wmt-1.66
%P 800-805
Markdown (Informal)
[MEE4 and XLsim : IIIT HYD’s Submissions’ for WMT23 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.66) (Mukherjee & Shrivastava, WMT 2023)
ACL