Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification

Robert Flynn, Matthew Shardlow


Abstract
We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features as inputs. Our system displays reasonable results with a Pearson correlation of 0.7754 on the task as a whole. We highlight the limitations of this method in properly assessing the context of the target text, and explore the effectiveness of both systems across a range of genres. Both models were submitted as part of LCP 2021, which focuses on the identification of complex words and phrases as a context dependent, regression based task.
Anthology ID:
2021.semeval-1.76
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
603–608
Language:
URL:
https://aclanthology.org/2021.semeval-1.76
DOI:
10.18653/v1/2021.semeval-1.76
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.76.pdf