Add bibtex

README.md

@@ -15,7 +15,7 @@ Propaganda Techniques Analysis BERT
 ----
 
 This model is a BERT based model to make predictions of propaganda techniques in
-news articles in English. It was introduced in
+news articles in English. The model is described in
 [this paper](https://propaganda.qcri.org/papers/EMNLP_2019__Fine_Grained_Propaganda_Detection.pdf).
 
 
@@ -24,8 +24,10 @@ news articles in English. It was introduced in
 Please find propaganda definition here:
 https://propaganda.qcri.org/annotations/definitions.html
 
+You can also try the model in action here: https://www.tanbih.org/prta
 
-## How to use
+
+### How to use
 
 ```python
 >>> from transformers import BertTokenizerFast
@@ -45,3 +47,26 @@ https://propaganda.qcri.org/annotations/definitions.html
 >>> tokens = tokenizer.convert_ids_to_tokens(inputs.input_ids[0][1:-1])
 >>> tags = [model.token_tags[i] for i in token_class_index[0].tolist()[1:-1]]
 ```
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{da-san-martino-etal-2019-fine,
+    title = "Fine-Grained Analysis of Propaganda in News Article",
+    author = "Da San Martino, Giovanni  and
+      Yu, Seunghak  and
+      Barr{\'o}n-Cede{\~n}o, Alberto  and
+      Petrov, Rostislav  and
+      Nakov, Preslav",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D19-1565",
+    doi = "10.18653/v1/D19-1565",
+    pages = "5636--5646",
+    abstract = "Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at fragment level with eighteen propaganda techniques and propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.",
+}
+```