@inproceedings{pandey-etal-2022-multilinguals,
title = "Multilinguals at {S}em{E}val-2022 Task 11: Complex {NER} in Semantically Ambiguous Settings for Low Resource Languages",
author = "Pandey, Amit and
Daw, Swayatta and
Unnam, Narendra and
Pudi, Vikram",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.201",
doi = "10.18653/v1/2022.semeval-1.201",
pages = "1469--1476",
abstract = "We leverage pre-trained language models to solve the task of complex NER for two low-resource languages: Chinese and Spanish. We use the technique of Whole Word Masking (WWM) to boost the performance of masked language modeling objective on large and unsupervised corpora. We experiment with multiple neural network architectures, incorporating CRF, BiLSTMs, and Linear Classifiers on top of a fine-tuned BERT layer. All our models outperform the baseline by a significant margin and our best performing model obtains a competitive position on the evaluation leaderboard for the blind test set.",
}
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%0 Conference Proceedings
%T Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages
%A Pandey, Amit
%A Daw, Swayatta
%A Unnam, Narendra
%A Pudi, Vikram
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F pandey-etal-2022-multilinguals
%X We leverage pre-trained language models to solve the task of complex NER for two low-resource languages: Chinese and Spanish. We use the technique of Whole Word Masking (WWM) to boost the performance of masked language modeling objective on large and unsupervised corpora. We experiment with multiple neural network architectures, incorporating CRF, BiLSTMs, and Linear Classifiers on top of a fine-tuned BERT layer. All our models outperform the baseline by a significant margin and our best performing model obtains a competitive position on the evaluation leaderboard for the blind test set.
%R 10.18653/v1/2022.semeval-1.201
%U https://aclanthology.org/2022.semeval-1.201
%U https://doi.org/10.18653/v1/2022.semeval-1.201
%P 1469-1476
Markdown (Informal)
[Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages](https://aclanthology.org/2022.semeval-1.201) (Pandey et al., SemEval 2022)
ACL