@inproceedings{schram-etal-2023-performance,
title = "Performance Prediction via {B}ayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks",
author = "Schram, Viktoria and
Beck, Daniel and
Cohn, Trevor",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.131",
doi = "10.18653/v1/2023.eacl-main.131",
pages = "1790--1801",
abstract = "Performance prediction for Natural Language Processing (NLP) seeks to reduce the experimental burden resulting from the myriad of different evaluation scenarios, e.g., the combination of languages used in multilingual transfer. In this work, we explore the framework ofBayesian matrix factorisation for performance prediction, as many experimental settings in NLP can be naturally represented in matrix format. Our approach outperforms the state-of-the-art in several NLP benchmarks, including machine translation and cross-lingual entity linking. Furthermore, it also avoids hyperparameter tuning and is able to provide uncertainty estimates over predictions.",
}
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%0 Conference Proceedings
%T Performance Prediction via Bayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks
%A Schram, Viktoria
%A Beck, Daniel
%A Cohn, Trevor
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F schram-etal-2023-performance
%X Performance prediction for Natural Language Processing (NLP) seeks to reduce the experimental burden resulting from the myriad of different evaluation scenarios, e.g., the combination of languages used in multilingual transfer. In this work, we explore the framework ofBayesian matrix factorisation for performance prediction, as many experimental settings in NLP can be naturally represented in matrix format. Our approach outperforms the state-of-the-art in several NLP benchmarks, including machine translation and cross-lingual entity linking. Furthermore, it also avoids hyperparameter tuning and is able to provide uncertainty estimates over predictions.
%R 10.18653/v1/2023.eacl-main.131
%U https://aclanthology.org/2023.eacl-main.131
%U https://doi.org/10.18653/v1/2023.eacl-main.131
%P 1790-1801
Markdown (Informal)
[Performance Prediction via Bayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks](https://aclanthology.org/2023.eacl-main.131) (Schram et al., EACL 2023)
ACL