# Interpreting Verbal Metaphors by Paraphrasing

arXiv: https://arxiv.org/abs/2104.03391 · HTML: https://mlsanity.com/paper/2104.03391/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)

## Built on by (most cited first, 4 of 4)

- [Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings](https://mlsanity.com/paper/2209.07494/) (2022)
- [Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding](https://mlsanity.com/paper/2507.15357/) (2025)
- [Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation](https://mlsanity.com/paper/2404.07053/) (2024)
- [On the Use of Metaphor Translation in Psychiatry](https://mlsanity.com/paper/2312.14845/) (2023)

## Abstract (arXiv)

Metaphorical expressions are difficult linguistic phenomena, challenging diverse Natural Language Processing tasks. Previous works showed that paraphrasing a metaphor as its literal counterpart can help machines better process metaphors on downstream tasks. In this paper, we interpret metaphors with BERT and WordNet hypernyms and synonyms in an unsupervised manner, showing that our method significantly outperforms the state-of-the-art baseline. We also demonstrate that our method can help a machine translation system improve its accuracy in translating English metaphors to 8 target languages.