@inproceedings{whistely-etal-2022-presiuniv,
title = "{P}resi{U}niv at {TSAR}-2022 Shared Task: Generation and Ranking of Simplification Substitutes of Complex Words in Multiple Languages",
author = "Whistely, Peniel and
Mathias, Sandeep and
Poornima, Galiveeti",
editor = "{\v{S}}tajner, Sanja and
Saggion, Horacio and
Ferr{\'e}s, Daniel and
Shardlow, Matthew and
Sheang, Kim Cheng and
North, Kai and
Zampieri, Marcos and
Xu, Wei",
booktitle = "Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.tsar-1.22",
doi = "10.18653/v1/2022.tsar-1.22",
pages = "213--217",
abstract = "In this paper, we describe our approach to generate and rank candidate simplifications using pre-trained language models (Eg. BERT), publicly available word embeddings (Eg. FastText), and a part-of-speech tagger, to generate and rank candidate contextual simplifications for a given complex word. In this task, our system, PresiUniv, was placed first in the Spanish track, 5th in the Brazilian-Portuguese track, and 10th in the English track. We upload our codes and data for this project to aid in replication of our results. We also analyze some of the errors and describe design decisions which we took while writing the paper.",
}
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%0 Conference Proceedings
%T PresiUniv at TSAR-2022 Shared Task: Generation and Ranking of Simplification Substitutes of Complex Words in Multiple Languages
%A Whistely, Peniel
%A Mathias, Sandeep
%A Poornima, Galiveeti
%Y Štajner, Sanja
%Y Saggion, Horacio
%Y Ferrés, Daniel
%Y Shardlow, Matthew
%Y Sheang, Kim Cheng
%Y North, Kai
%Y Zampieri, Marcos
%Y Xu, Wei
%S Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Virtual)
%F whistely-etal-2022-presiuniv
%X In this paper, we describe our approach to generate and rank candidate simplifications using pre-trained language models (Eg. BERT), publicly available word embeddings (Eg. FastText), and a part-of-speech tagger, to generate and rank candidate contextual simplifications for a given complex word. In this task, our system, PresiUniv, was placed first in the Spanish track, 5th in the Brazilian-Portuguese track, and 10th in the English track. We upload our codes and data for this project to aid in replication of our results. We also analyze some of the errors and describe design decisions which we took while writing the paper.
%R 10.18653/v1/2022.tsar-1.22
%U https://aclanthology.org/2022.tsar-1.22
%U https://doi.org/10.18653/v1/2022.tsar-1.22
%P 213-217
Markdown (Informal)
[PresiUniv at TSAR-2022 Shared Task: Generation and Ranking of Simplification Substitutes of Complex Words in Multiple Languages](https://aclanthology.org/2022.tsar-1.22) (Whistely et al., TSAR 2022)
ACL