|
--- |
|
license: cc-by-nc-sa-3.0 |
|
metrics: |
|
- f1 |
|
- accuracy |
|
widget: |
|
- text: "We are at a relationship crossroad" |
|
example_title: "Metaphoric1" |
|
- text: "The car waits at a crossroad" |
|
example_title: "Literal1" |
|
- text: "I win the argument" |
|
example_title: "Metaphoric2" |
|
- text: "I win the game" |
|
example_title: "Literal2" |
|
--- |
|
|
|
# Multilingual-Metaphor-Detection |
|
|
|
This page provides a fine-tuned multilingual language model [XLM-RoBERTa](https://arxiv.org/pdf/1911.02116.pdf) for metaphor detection on a token-level using the [Huggingface token-classification approach](https://huggingface.co/tasks/token-classification). Label 1 corresponds to metaphoric usage. |
|
|
|
# Dataset |
|
The dataset the model is trained on is the [VU Amsterdam Metaphor Corpus](http://www.vismet.org/metcor/documentation/home.html) that was annotated on a word-level following the metaphor identification protocol. The training corpus is restricted to English, however, XLM-R shows decent zero-shot performances when tested on other languages. |
|
|
|
# Results |
|
Following the evaluation criteria from the [2020 Second Shared Task on Metaphor detection](https://competitions.codalab.org/competitions/22188#results) our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-R<sub>Base</sub> and 0.77 when training XLM-R<sub>Large.</sub>. |
|
|
|
We train for 8 epochs loading the model with the best evaluation performance at the end and using a learning rate of 2e-5. From the allocated training data 10% are utilized for validation while the final test set is being kept seperate and only used for the final evaluation. |
|
|
|
# Code for Training and Reference |
|
The training and evaluation code is available on [Github](https://github.com/lwachowiak/Multilingual-Metaphor-Detection/). |
|
Our [paper](https://aclanthology.org/2022.flp-1.7/) describing training and model application is available online: |
|
>@inproceedings{wachowiak2022drum, |
|
> title={Drum Up SUPPORT: Systematic Analysis of Image-Schematic Conceptual Metaphors}, |
|
> author={Wachowiak, Lennart and Gromann, Dagmar and Xu, Chao}, |
|
> booktitle={Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)}, |
|
> pages={44--53}, |
|
> year={2022} |
|
>} |
|
|
