Update README.md

README.md

@@ -18,12 +18,16 @@ The corpus has a CC-BY 4.0 license. Download EsCoLA inDomain train and dev datas
 
 Data format
 
-EsCoLA dataset is split into two subsets: an in-domain subset (InDomain) with 10,567 sentences, and an out-of-domain subset (OutDomain) with 607 sentences. The in-domain subset has been split five times into train/dev/test sections:
+EsCoLA dataset is split into two subsets: an in-domain subset (InDomain) with 10,567 sentences, and an out-of-domain subset (OutDomain) with 607 sentences. 
+The in-domain subset has been split into train/dev/test sections:
 
 train: 8454 sentences
+
 dev:   1053 sentences
+
 test:  1060 sentences 
 
+
 And the out-of-domain subset is split into dev/test sections. The test sets are not made public.
 
 For the in-domain subset, each line in the .tsv files consists of 11 tab-separated columns:
@@ -46,6 +50,7 @@ Column 3: the acceptability judgment label from the source (0=unacceptable, 1=ac
 Column 4: the source's annotation (* for the unacceptable sentences)
 Column 5: the sentence
 Column 6: the category of the linguistic phenomenon the sentence is an example of
+
 Corpus Sample
 
 In-domain:
@@ -67,14 +72,16 @@ OD_5 ng34 0 * Dudo tu solución. 1
 
 Processing
 
-During gathering of the data and processing, some sentences from the source documents may have been omitted or altered. We discarded examples marking dubious acceptability with "?" or other signs, but those examples that included acceptability alternations were taken by creating the two versions: the acceptable and the unacceptable sentence. Finally, the examples that were not full sentences, that is, that contain no main verb, were manually edited to add a neutral verb to convert them into sentences, while keeping the acceptability value.
+During the gathering of the data and their processing, some sentences from the source documents may have been omitted or altered. We discarded examples marking dubious acceptability with "?" or other signs, but those examples that included acceptability alternations were taken by creating the two versions: the acceptable and the unacceptable sentence. Finally, the examples that were not full sentences, that is, that contain no main verb, were manually edited to add a neutral verb to convert them into sentences, while keeping the acceptability value.
 
 Sources
 
 InDomain: Demonte and Bosque (1999)
 OutDomain: RAE (2009), Palencia and Aragonés (2007) Díaz and Yagüe (2019)
 
-Annotation The dataset has been manually annotated with 14 linguistic phenomena.
+Annotation 
+
+The dataset has been manually annotated with 14 linguistic phenomena.
 
 1. Simple
 2. Predicative
@@ -103,7 +110,7 @@ Please, if you use the dataset cite the following papers:
 
 Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2018. Neural network acceptability judgments. arXiv preprint arXiv:1805.12471.
 
-Núria Bel, Marta Punsola, Valle Ruiz-Fernández, 2024, EsCoLA: Spanish Corpus of Linguistic Acceptability. Joint International Conference on Computational Linguistics, Language Resources and Evaluation LREC-COLING 2024. Torino. Italy.
+Núria Bel, Marta Punsola, Valle Ruiz-Fernández, 2024, EsCoLA: Spanish Corpus of Linguistic Acceptability. Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation LREC-COLING 2024. Torino. Italy.
 
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