Update README.md

README.md

@@ -796,7 +796,7 @@ configs:
 # Portuguese Benchmark
 
 This a collection of datasets in Portuguese initially meant to train and evaluate supervised language models such as BERT, RoBERTa, etc...
-It contains 9 datasets and 17 Tasks for Classification, NLI, Semantic Similarity Scoring and Named-Entity Recognition.
+It contains 9 datasets and 17 Tasks for Classification (CLS), NLI, Semantic Similarity Scoring (STS) and Named-Entity Recognition (NER).
 
 
 ## Tasks:
@@ -810,7 +810,7 @@ for persons, locations, time entities, organizations, legislation and legal case
 such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. 
 In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents.
 
-**assin2-rte** and **assin2-sts** (RTE/STS) [\[Link\]](https://sites.google.com/view/assin2)
+**assin2-rte** and **assin2-sts** (NLI/STS) [\[Link\]](https://sites.google.com/view/assin2)
 
 The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
 The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
@@ -845,7 +845,7 @@ also known as job requests made by the parliamentarians.
 The law product entity refers to systems, programs, and other products created
 from legislation.
 
-**brazilian_court_decisions_judgment** and **brazilian_court_decisions_unanimity** (CLASSIFICATION) [\[Link\]](https://github.com/lagefreitas/predicting-brazilian-court-decisions)
+**brazilian_court_decisions_judgment** and **brazilian_court_decisions_unanimity** (CLS) [\[Link\]](https://github.com/lagefreitas/predicting-brazilian-court-decisions)
 
 The dataset is a collection of 4043 Ementa (summary) court decisions and their metadata from the Tribunal de 
 Justiça de Alagoas (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled according 
@@ -864,7 +864,7 @@ The dataset version processed here ONLY USE the "Category" level of the original
 [1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese."
 Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.
 
-**multi_eurlex_pt** (MULTILABEL CLASSIFICATION) [\[Link\]](https://github.com/nlpaueb/MultiEURLEX/)
+**multi_eurlex_pt** (Multilabel CLS) [\[Link\]](https://github.com/nlpaueb/MultiEURLEX/)
 
 MultiEURLEX comprises 65k EU laws in 23 official EU languages. 
 Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. 
@@ -881,7 +881,7 @@ of the European Union. The documents have been annotated for named entities foll
 guidelines of the MAPA project which foresees two annotation level, a general and a more 
 fine-grained one. The annotated corpus can be used for named entity recognition/classification.
 
-**Portuguese_Hate_Speech_binary** (CLASSIFICATION) [\[Link\]](https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset)
+**Portuguese_Hate_Speech_binary** (CLS) [\[Link\]](https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset)
 
 The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by
 annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary
@@ -892,6 +892,17 @@ than others and that their detection depends on personal perception. This hierar
 the main contribution of the presented work, as it facilitates the identification of different types of
 hate speech and their intersections.
 
+**rrip** (CLS) [\[Link\]](https://bit.ly/rhetoricalrole)
+
+Rhetorical role identification (RRI) is an NLP task that consists of labeling the sentences of a document according 
+to a given set of semantic functions (rhetorical roles). This task is useful to applications like document summarization, 
+semantic search, document analysis, among others. 
+In this corpus, we propose to segment petitions into eight rhetorical roles by mainly considering the analytic needs of 
+judge's' offices in Brazil. We present a corpus of 70 petitions comprising more than 10 thousand sentences manually labeled 
+with the proposed rhetorical roles. These petitions were taken from civil lawsuits filed in the court of the Brazilian state 
+of Mato Grosso do Sul (TJMS).
+ 
+
 
 ## Citations
 
@@ -1075,4 +1086,18 @@ Citation for each one of the Tasks:
  doi = "10.18653/v1/W19-3510",
  pages = "94--104"
 }
+
+#rrip
+@inproceedings{aragy_rhetorical_2021,
+ series = {Lecture {Notes} in {Computer} {Science}},
+ title = {Rhetorical {Role} {Identification} for {Portuguese} {Legal} {Documents}},
+ isbn = {978-3-030-91699-2},
+ doi = {10.1007/978-3-030-91699-2_38},
+ booktitle = {Intelligent {Systems}},
+ publisher = {Springer International Publishing},
+ author = {Aragy, Roberto and Fernandes, Eraldo Rezende and Caceres, Edson Norberto},
+ editor = {Britto, André and Valdivia Delgado, Karina},
+ year = {2021},
+ pages = {557--571},
+}
 ```