Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages

Amit Pandey, Swayatta Daw, Narendra Babu Unnam, Vikram Pudi
7/14/2022
cs.CLcs.AIcs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Trained on 100 million words and still in shape: BERT meets British National Corpus

86% match1 repo342023

Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]

85% match1 repo122023

Neural Architecture Search for Sentence Classification with BERT

85% match2024

PuoBERTa: Training and evaluation of a curated language model for Setswana

84% match2 repos62023

Investigating Masking-based Data Generation in Language Models

84% match2023

ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding

84% match2023

Cite this paper

@article{pandey2022multilinguals,
  title  = {Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages},
  author = {Amit Pandey and Swayatta Daw and Narendra Babu Unnam and Vikram Pudi},
  year   = {2022},
  eprint = {2207.06882},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.06882v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2207.06882.svg)](https://codeofpaper.com/paper/2207.06882)

Discussion