data card.

README.md

@@ -187,12 +187,11 @@ The authors first crowd-sourced a set of manual alignments between sentences in
 
 The trained alignment prediction model was then applied to the other articles in Simple English Wikipedia with an English counterpart to create a larger corpus of aligned sentences (corresponding to the `auto` and `auto_acl` configs here).
 
-[ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus]( https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence
+[ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus](https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence
 splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations.
 
 TURKCorpus is a high quality simplification dataset where each source (not simple) sentence is associated with 8 human-written simplifications that focus on lexical paraphrasing. It is one of the two evaluation datasets for the text simplification task in GEM. It acts as the validation and test set for paraphrasing-based simplification that does not involve sentence splitting and deletion.
 
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 #### Add. License Info
 
 <!-- info: What is the 'other' license of the dataset? -->

wiki_auto_asset_turk.json

@@ -20,7 +20,7 @@
       ],
       "language-dialects": "n/a",
       "language-speakers": "Wiki-Auto contains English text only (BCP-47: `en`). It is presented as a translation task where Wikipedia Simple English is treated as its own idiom. For a statement of what is intended (but not always observed) to constitute Simple English on this platform, see [Simple English in Wikipedia](https://simple.wikipedia.org/wiki/Wikipedia:About#Simple_English).\nBoth ASSET and TURK use crowdsourcing to change references, and their language is thus a combination of the WikiAuto data and the language of the demographic on mechanical Turk",
-      "intended-use": "WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia as a resource to train sentence simplification systems.\n\nThe authors first crowd-sourced a set of manual alignments between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia (this corresponds to the `manual` config in this version of the dataset), then trained a neural CRF system to predict these alignments.\n\nThe trained alignment prediction model was then applied to the other articles in Simple English Wikipedia with an English counterpart to create a larger corpus of aligned sentences (corresponding to the `auto` and `auto_acl` configs here).\n\n[ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus]( https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence\nsplitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations.\n\nTURKCorpus is a high quality simplification dataset where each source (not simple) sentence is associated with 8 human-written simplifications that focus on lexical paraphrasing. It is one of the two evaluation datasets for the text simplification task in GEM. It acts as the validation and test set for paraphrasing-based simplification that does not involve sentence splitting and deletion.\n",
+      "intended-use": "WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia as a resource to train sentence simplification systems.\n\nThe authors first crowd-sourced a set of manual alignments between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia (this corresponds to the `manual` config in this version of the dataset), then trained a neural CRF system to predict these alignments.\n\nThe trained alignment prediction model was then applied to the other articles in Simple English Wikipedia with an English counterpart to create a larger corpus of aligned sentences (corresponding to the `auto` and `auto_acl` configs here).\n\n[ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus](https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence\nsplitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations.\n\nTURKCorpus is a high quality simplification dataset where each source (not simple) sentence is associated with 8 human-written simplifications that focus on lexical paraphrasing. It is one of the two evaluation datasets for the text simplification task in GEM. It acts as the validation and test set for paraphrasing-based simplification that does not involve sentence splitting and deletion.",
       "license-other": "WikiAuto: `CC BY-NC 3.0`, ASSET: `CC BY-NC 4.0`, TURK: `GNU General Public License v3.0`",
       "task": "Simplification",
       "communicative": "The goal is to communicate the main ideas of source sentence in a way that is easier to understand by non-native speakers of English.\n"