Add multilingual info to readme

README.md

@@ -24,10 +24,10 @@ Compared to previous resources for this task, the resulting dataset was found to
 
 ### Why is this dataset part of GEM?
 
-**BiSECT** is the largest available corpora for the Split and Rephrase task. In addition, it has been shown that **BiSECT** is of higher quality than previous Split and Rephrase corpora and contains a wider variety of splitting operations.
+**BiSECT** is the largest available corpora for the Split and Rephrase task. In addition, it has been shown that **BiSECT** is of higher quality than previous Split and Rephrase corpora, contains a wider variety of splitting operations, and is also available in four languages.
 
 ### Languages
-English (en-US). Split and Rephase pairs for French, Spanish, and German are also available upon request.
+**BiSECT** is available in english (en-US), French, Spanish, German.
 
 ## Meta Information
 
@@ -129,7 +129,7 @@ The goal of Split and Rephrase is to break down longer sentences into multiple s
 
 #### Initial Data Collection and Normalization
 
-The construction of the **BiSECT** corpus relies on leveraging the sentence-level alignments from [OPUS](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf)), a collection of bilingual parallel corpora over many language pairs. Given a target language *A*, this work extracts all 1-2 and 2-1 sentence alignments from parallel corpora between *A* and a set of foreign languages ***B***.
+The construction of the **BiSECT** corpus relies on leveraging the sentence-level alignments from [*OPUS*](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf)), a collection of bilingual parallel corpora over many language pairs. Given a target language *A*, this work extracts all 1-2 and 2-1 sentence alignments from parallel corpora between *A* and a set of foreign languages ***B***.
 
 Next, the foreign sentences are translated into English using Google Translate's [Web API service](https://pypi.org/project/googletrans/) to obtain sentence alignments between a single long sentence $l$ and two corresponding split sentences $s= (s_1, s_2)$, both in the desired language.
 
@@ -137,13 +137,13 @@ To remove noise, the authors remove pairs where $l$ contains a token with a punc
 
 #### Who are the source language producers?
 
-Opus corpora are from a variety of sources. The **BiSECT** training set contains pairs extracted from five datasets: *CCAligned*, parallel English-French documents from common crawl; *Europarl*, an English-French dataset from European Parliament; *10^9 FR-EN*, an English-French newswire corpus; *ParaCrawl*, a multilingual web crawl dataset; and *UN*, multilingual translated UN documents. The **BiSECT** test set contains pairs extracted from two additional datasets: *EMEA*, an English-French parallel corpus made out of PDF documents from the European Medicines Agency; and *JRC-Acquis*, a multilingual collection of European Union legislative text.
+Opus corpora are from a variety of sources. The **BiSECT** English training set contains pairs extracted from five datasets: *CCAligned*, parallel English-French documents from common crawl; *Europarl*, an English-French dataset from European Parliament; *10^9 FR-EN*, an English-French newswire corpus; *ParaCrawl*, a multilingual web crawl dataset; and *UN*, multilingual translated UN documents. The **BiSECT** English test set contains pairs extracted from two additional datasets: *EMEA*, an English-French parallel corpus made out of PDF documents from the European Medicines Agency; and *JRC-Acquis*, a multilingual collection of European Union legislative text. Details about the French, Spanish, and German versions can be found in the paper.
 
 ### Annotations
 
 #### Annotation process
 
-The training data was automatically extracted, so no annotators were needed. For the test set, the authors manually selected 583 high-quality sentence splits from 1000 random source-target pairs from the *EMEA* and *JRC-Acquis* corpora.
+The training data was automatically extracted, so no annotators were needed. For the English test set, the authors manually selected 583 high-quality sentence splits from 1000 random source-target pairs from the *EMEA* and *JRC-Acquis* corpora.
 
 #### Who are the annotators?
 
@@ -151,17 +151,17 @@ None.
 
 ### Personal and Sensitive Information
 
-Since this data is collected from [OPUS](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf), all pairs are already in the public domain.
+Since this data is collected from [*OPUS*](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf), all pairs are already in the public domain.
 
 ## Changes to the Original Dataset for GEM
 
-The original **BiSECT** training, validation, and test splits are maintained to ensure a fair comparison. Note that the original **BiSECT** test set was created by manually selecting 583 high-quality Split and Rephrase instances from 1000 random source-target pairs sampled from the *EMEA* and *JRC-Acquis* corpora from [OPUS](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf).
+The original **BiSECT** training, validation, and test splits are maintained in each language to ensure a fair comparison. Note that the original **BiSECT** English test set was created by manually selecting 583 high-quality Split and Rephrase instances from 1000 random source-target pairs sampled from the *EMEA* and *JRC-Acquis* corpora from [*OPUS*](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf).
 
-As the first challenge set, we include the *HSPLIT-Wiki* test set, containing 359 pairs. For each complex sentence, there are four reference splits; To ensure replicability, as reference splits, we again follow the BiSECT paper and present only the references from [HSplit2-full](https://github.com/eliorsulem/HSplit-corpus/blob/master/HSplit/HSplit2_full). 
+As the first English challenge set, we include the *HSPLIT-Wiki* test set, containing 359 pairs. For each complex sentence, there are four reference splits; To ensure replicability, as reference splits, we again follow the BiSECT paper and present only the references from [HSplit2-full](https://github.com/eliorsulem/HSplit-corpus/blob/master/HSplit/HSplit2_full). 
 
 ### Special Test Sets
 
-In addition to the two evaluation sets used in the original **BiSECT** paper, we also introduce a second challenge set. For this, we initially consider all 7,293 pairs from the *EMEA* and *JRC-Acquis* corpora. From there, we classify each pair using the classification algorithm from Section 4.2 of the original **BiSECT** paper. The three classes are as follows:
+In addition to the two evaluation sets used in the original **BiSECT** paper, we also introduce a second English challenge set. For this, we initially consider all 7,293 pairs from the *EMEA* and *JRC-Acquis* corpora. From there, we classify each pair using the classification algorithm from Section 4.2 of the original **BiSECT** paper. The three classes are as follows:
 
 1) **Direct Insertion**: when a long sentence *l* contains two independent clauses and requires only minor changes in order to make a fluent and meaning-preserving split *s*.
 2) **Changes near Split**, when *l* contains one independent and one dependent clause, but modifications are restricted to the region where *l* is split.
@@ -175,7 +175,7 @@ We keep only pairs labeled as Type 3, and after filtering out pairs with signifi
 Understanding long and complex sentences is challenging for both humans and NLP models. The **BiSECT** dataset helps facilitate more research on Split and Rephrase as a task within itself, as well as how it can benefit downstream NLP applications.
 
 ### Impact on Underserved Communities
-The data as provided in GEMv2 is in English, which is a language with abundant existing resources. However, the original paper also provides Split and Rephrase pairs for French, Spanish, and German, while providing a framework for leveraging bilingual corpora from any language pair found within [OPUS](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf).
+The data as provided in GEMv2 is in English, French, Spanish, and German, languages with abundant existing resources. However, the dataset creation process introduced in the original paper provides a framework for leveraging bilingual corpora from any language pair found within [*OPUS*](http://www.lrec-conf.org/proceedings/lrec2004/pdf/320.pdf).
 
 ### Discussion of Biases