cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora

by   Abhilash Nandy, et al.

This paper describes the performance of the team cs60075_team2 at SemEval 2021 Task 1 - Lexical Complexity Prediction. The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).


page 1

page 2

page 3

page 4


Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models

This paper describes Galileo's performance in SemEval-2020 Task 12 on de...

UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction

Reading is a complex process which requires proper understanding of text...

Modeling Event Plausibility with Consistent Conceptual Abstraction

Understanding natural language requires common sense, one aspect of whic...

Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations

Constructing accurate and automatic solvers of math word problems has pr...

PathologyBERT – Pre-trained Vs. A New Transformer Language Model for Pathology Domain

Pathology text mining is a challenging task given the reporting variabil...

Use of Transformer-Based Models for Word-Level Transliteration of the Book of the Dean of Lismore

The Book of the Dean of Lismore (BDL) is a 16th-century Scottish Gaelic ...

Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models

Text analysis of social media for sentiment, topic analysis, and other a...