TL;DR
This paper introduces a BERT-based end-to-end method for location metonymy resolution that outperforms existing models and does not rely on external resources or handcrafted features.
Contribution
The authors present a novel word-level classification approach using only BERT, eliminating the need for external lexical resources or taggers.
Findings
Achieves state-of-the-art results on 5 datasets
Outperforms conventional BERT models and benchmarks
Generalizes well to unseen data
Abstract
Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources. In this paper, we propose an end-to-end word-level classification approach based only on BERT, without dependencies on taggers, parsers, curated dictionaries of place names, or other external resources. We show that our approach achieves the state-of-the-art on 5 datasets, surpassing conventional BERT models and benchmarks by a large margin. We also show that our approach generalises well to unseen data.
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Taxonomy
MethodsLinear Layer · Linear Warmup With Linear Decay · Multi-Head Attention · Weight Decay · Attention Is All You Need · Residual Connection · Attention Dropout · Layer Normalization · WordPiece · Adam
