Update README.md

README.md

@@ -79,6 +79,8 @@ For each of 9 languages, we collected 1k pairs of toxic<->detoxified instances s
 
 📰 **Updates**
 
+**[2025]** We dived into the explainability of our data in our new [COLING paper](https://huggingface.co/papers/2412.11691)!
+
 **[2024]** You can check additional releases for [Ukrainian ParaDetox](https://huggingface.co/datasets/textdetox/uk_paradetox) and [Spanish ParaDetox](https://huggingface.co/datasets/textdetox/es_paradetox) from NAACL 2024!
 
 **[2024]** **April, 23rd, update: We are realsing the parallel dev set! The test part for the final phase of the competition is available [here](https://huggingface.co/datasets/textdetox/multilingual_paradetox_test)!!!**
@@ -100,6 +102,40 @@ The list of the sources for the original toxic sentences:
 ## Citation
 If you would like to acknowledge our work, please, cite the following manuscripts:
 
+```
+@inproceedings{dementieva-etal-2025-multilingual,
+    title = "Multilingual and Explainable Text Detoxification with Parallel Corpora",
+    author = "Dementieva, Daryna  and
+      Babakov, Nikolay  and
+      Ronen, Amit  and
+      Ayele, Abinew Ali  and
+      Rizwan, Naquee  and
+      Schneider, Florian  and
+      Wang, Xintong  and
+      Yimam, Seid Muhie  and
+      Moskovskiy, Daniil Alekhseevich  and
+      Stakovskii, Elisei  and
+      Kaufman, Eran  and
+      Elnagar, Ashraf  and
+      Mukherjee, Animesh  and
+      Panchenko, Alexander",
+    editor = "Rambow, Owen  and
+      Wanner, Leo  and
+      Apidianaki, Marianna  and
+      Al-Khalifa, Hend  and
+      Eugenio, Barbara Di  and
+      Schockaert, Steven",
+    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
+    month = jan,
+    year = "2025",
+    address = "Abu Dhabi, UAE",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.coling-main.535/",
+    pages = "7998--8025",
+    abstract = "Even with various regulations in place across countries and social media platforms (Government of India, 2021; European Parliament and Council of the European Union, 2022), digital abusive speech remains a significant issue. One potential approach to address this challenge is automatic text detoxification, a text style transfer (TST) approach that transforms toxic language into a more neutral or non-toxic form. To date, the availability of parallel corpora for the text detoxification task (Logacheva et al., 2022; Atwell et al., 2022; Dementieva et al., 2024a) has proven to be crucial for state-of-the-art approaches. With this work, we extend parallel text detoxification corpus to new languages{---}German, Chinese, Arabic, Hindi, and Amharic{---}testing in the extensive multilingual setup TST baselines. Next, we conduct the first of its kind an automated, explainable analysis of the descriptive features of both toxic and non-toxic sentences, diving deeply into the nuances, similarities, and differences of toxicity and detoxification across 9 languages. Finally, based on the obtained insights, we experiment with a novel text detoxification method inspired by the Chain-of-Thoughts reasoning approach, enhancing the prompting process through clustering on relevant descriptive attributes."
+}
+```
+
 ```
 @inproceedings{dementieva2024overview,
   title={Overview of the Multilingual Text Detoxification Task at PAN 2024},