Skip to content
conftrace_
2020 SemEval SemEval 2020

IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection

Abstract

AbstractWe propose an end-to-end model that takes as input the text and corresponding to each word gives the probability of the word to be emphasized. Our results show that transformer-based models are particularly effective in this task. We achieved an evaluation score of 0.810 and were ranked third on the leaderboard.

🐣 Hot Topic Early Bird - transformer-based model

Authors

Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Transformers

Keywords

text classification transformer-based model text processing transformer model neural network emphasis selection word probability
Download PDF

More from SemEval 2020

All SemEval 2020 papers →
EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection
Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models
LAST at SemEval-2020 Task 10: Finding Tokens to Emphasise in Short Written Texts with Precomputed Embedding Models and LightGBM
MIDAS at SemEval-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings
Randomseed19 at SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media