IlliniMet: Illinois System for Metaphor Detection with Contextual and Linguistic Information

WS 2020 · Hongyu Gong, Kshitij Gupta, Akriti Jain, Suma Bhat ·

Metaphors are rhetorical use of words based on the conceptual mapping as opposed to their literal use. Metaphor detection, an important task in language understanding, aims to identify metaphors in word level from given sentences. We present IlliniMet, a system to automatically detect metaphorical words. Our model combines the strengths of the contextualized representation by the widely used RoBERTa model and the rich linguistic information from external resources such as WordNet. The proposed approach is shown to outperform strong baselines on a benchmark dataset. Our best model achieves F1 scores of 73.0{\%} on VUA ALLPOS, 77.1{\%} on VUA VERB, 70.3{\%} on TOEFL ALLPOS and 71.9{\%} on TOEFL VERB.

PDF Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Add Remove

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods

Add Remove

Adam • Attention Dropout • BERT • Dense Connections • Dropout • GELU • Layer Normalization • Linear Layer • Linear Warmup With Linear Decay • Multi-Head Attention • Residual Connection • RoBERTa • Scaled Dot-Product Attention • Softmax • Weight Decay • WordPiece

Edit Social Preview

IlliniMet: Illinois System for Metaphor Detection with Contextual and Linguistic Information

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit Add Remove

Methods Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Add Remove

Methods

Add Remove