Update README.md

README.md

@@ -1,30 +1,38 @@
----
-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
-The training and evaluation code is available on [Github](https://github.com/lwachowiak/Multilingual-Metaphor-Detection/)
+---
+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}
+>}