Update README.md

README.md

@@ -836,7 +836,7 @@ configs:
 
 Disrpt is a multilingual, multi-framework unified discourse analysis benchmark.
 
-This repo only contains the disrpt dataset when the underlying data is permissively licensed. Some datasets rely on corpora like the PTB.
+⚠️ This repo only contains the disrpt dataset when the underlying data is permissively licensed. Some datasets rely on corpora like the PTB.
 To load these datasets, do the following:
 `pip install disrpt-utils`
 
@@ -844,7 +844,7 @@ To load these datasets, do the following:
 from disrpt_utils import load_dataset
 
 corpora_paths={
-    #🚩 TODO Input your own paths 🚩
+    #TODO Input your own paths
     "tur.pdtb.tdb": "/mnt/nfs_share_magnet2/dsileo/data/disrpt/TDB1.0/raw/",
     "eng.rst.rstdt": "/mnt/nfs_share_magnet2/dsileo/data/disrpt/RSTtrees-WSJ-main-1.0-20231128T125253Z-001/"
 }
@@ -853,26 +853,22 @@ config_name="eng.rst.rstdt"
 load_dataset(config_name,'conllu',corpora_paths=corpora_paths)
 ```
 
-Otherwise, most datasets are 
+Otherwise, most datasets are directly accessible:
 ```python
 from huggingface import load_dataset
 load_dataset(/multilingual-discourse-hub/disrpt/'','eng.dep.scidtb.rels')
 ```
 
-scditb:
+# Citations
+
 ```
-@inproceedings{yang-li-2018-scidtb,
-    title = "{S}ci{DTB}: Discourse Dependency {T}ree{B}ank for Scientific Abstracts",
-    author = "Yang, An  and
-      Li, Sujian",
-    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
-    month = jul,
-    year = "2018",
-    address = "Melbourne, Australia",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/P18-2071",
-    doi = "10.18653/v1/P18-2071",
-    pages = "444--449",
-    abstract = "Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.",
+@inproceedings{braud2024disrpt,
+  title={{DISRPT}: A Multilingual, Multi-domain, Cross-framework Benchmark for Discourse Processing},
+  author={Braud, Chloé and Zeldes, Amir and Rivière, Laura and Liu, Yang Janet and Muller, Philippe and Sileo, Damien and Aoyama, Tatsuya},
+  booktitle={Proceedings of LREC-COLING 2024},
+  year={2024}
 }
-```
+```
+
+## Underline datasets:
+https://huggingface.co/datasets/multilingual-discourse-hub/disrpt/raw/main/bibtex.json