# FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning

arXiv: https://arxiv.org/abs/2302.04834 · HTML: https://mlsanity.com/paper/2302.04834/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://mlsanity.com/paper/2104.13615/) (2021)
- [Neural Metaphor Detection in Context](https://mlsanity.com/paper/1808.09653/) (2018)
- [Metaphor Generation with Conceptual Mappings](https://mlsanity.com/paper/2106.01228/) (2021)
- [Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting](https://mlsanity.com/paper/2105.11698/) (2021)
- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [The Secret of Metaphor on Expressing Stronger Emotion](https://mlsanity.com/paper/2301.13042/) (2023)
- [Nominal Metaphor Generation with Multitask Learning](https://mlsanity.com/paper/2206.05195/) (2022)
- [Symmetric Regularization based BERT for Pair-wise Semantic Reasoning](https://mlsanity.com/paper/1909.03405/) (2019)

## Built on by (most cited first, 18 of 18)

- [An Open Source Data Contamination Report for Large Language Models](https://mlsanity.com/paper/2310.17589/) (2023)
- [Metaphor Understanding Challenge Dataset for LLMs](https://mlsanity.com/paper/2403.11810/) (2024)
- [Metaphor Detection via Explicit Basic Meanings Modelling](https://mlsanity.com/paper/2305.17268/) (2023)
- [MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language](https://mlsanity.com/paper/2406.13698/) (2024)
- [Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing](https://mlsanity.com/paper/2407.11988/) (2024)
- [Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation](https://mlsanity.com/paper/2404.07053/) (2024)
- [When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models](https://mlsanity.com/paper/2502.13246/) (2025)
- [Finding Challenging Metaphors that Confuse Pretrained Language Models](https://mlsanity.com/paper/2401.16012/) (2024)
- [Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases](https://mlsanity.com/paper/2403.13901/) (2024)
- [ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure](https://mlsanity.com/paper/2309.03103/) (2023)
- [Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation](https://mlsanity.com/paper/2408.04378/) (2024)
- [Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames](https://mlsanity.com/paper/2604.20454/) (2026)
- [Enhancing multimodal analogical reasoning with Logic Augmented Generation](https://mlsanity.com/paper/2504.11190/) (2025)
- [Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders](https://mlsanity.com/paper/2507.06427/) (2025)
- [Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review](https://mlsanity.com/paper/2308.04306/) (2023)
- [The Medical Metaphors Corpus (MCC)](https://mlsanity.com/paper/2508.07993/) (2025)
- [Mechanistic Interpretability of Socio-Political Frames in Language Models](https://mlsanity.com/paper/2510.03799/) (2025)
- [The Multilingual FrameNet Corpus](https://mlsanity.com/paper/2608.23037/) (2026)

## Abstract (arXiv)

In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT not only achieves better or comparable performance to the state-of-the-art, but also is more explainable and interpretable compared to existing models, attributing to its ability of accounting for external knowledge of FrameNet.