Datasets:

huawei-noah
/

entity_cs

@@ -99,14 +99,12 @@ size_categories:
 ---
 # Dataset Card for EntityCS
-## Dataset Description
 - Repository: https://github.com/huawei-noah/noah-research/tree/master/NLP/EntityCS
 - Paper: https://aclanthology.org/2022.findings-emnlp.499.pdf
 - Point of Contact: [Fenia Christopoulou](mailto:efstathia.christopoulou@huawei.com), [Chenxi Whitehouse](mailto:chenxi.whitehouse@gmail.com)
-### Dataset Summary
 We use the English Wikipedia and leverage entity information from Wikidata to construct an entity-based Code Switching corpus.
 To achieve this, we make use of wikilinks in Wikipedia, i.e. links from one page to another.
@@ -122,18 +120,16 @@ Otherwise, we randomly select five target languages from the candidates.
 If no candidate languages can be found, we do not code-switch the sentence, instead, we keep it as part of the English corpus.
 Finally, we surround each entity with entity indicators (`<e>`, `</e>`).
-### Supported Tasks and Leaderboards
 The dataset was developped for intermediate pre-training of language models.
 In the paper we further fine-tune models on entity-centric downstream tasks, such as NER.
-### Languages
 The dataset covers 93 languages in total, including English.
-## Dataset Structure
-### Data Statistics
 | Statistic                     | Count       |
 |:------------------------------|------------:|
@@ -146,15 +142,15 @@ The dataset covers 93 languages in total, including English.
 | CS Sentences                  | 231,124,422 |
 | CS Entities                   | 420,907,878 |
-### Data Fields
-Each instance contains 3 fields:
 - `id`: Unique ID of each sentence
 - `language`: The language of choice for entity code-switching of the given sentence
 - `en_sentence`: The original English sentence
 - `cs_sentence`: The code-switched sentence
-In the case of the English subset, the `cs_sentence` field does not exist.
 An example of what a data instance looks like:
 ```
@@ -166,12 +162,12 @@ An example of what a data instance looks like:
 }
 ```
-### Data Splits
 There is a single data split for each language. You can randomly select a few examples from each language to serve as validation set.
-### Limitations
 An important limitation of the work is that before code-switching an entity, its morphological inflection is not checked.
 This can lead to potential errors as the form of the CS entity might not agree with the surrounding context (e.g. plural).
@@ -180,7 +176,9 @@ Secondly, the diversity of languages used to construct the EntityCS corpus is re
 This choice was for a better comparison between models, however it is possible to extend the corpus with more languages that XLM-R does not cover, following
 the procedure presented in the paper.
-### Citation
 ```html
 @inproceedings{whitehouse-etal-2022-entitycs,
@@ -197,3 +195,8 @@ the procedure presented in the paper.
     pages = "6698--6714"
 }
 ```

 ---
 # Dataset Card for EntityCS
 - Repository: https://github.com/huawei-noah/noah-research/tree/master/NLP/EntityCS
 - Paper: https://aclanthology.org/2022.findings-emnlp.499.pdf
 - Point of Contact: [Fenia Christopoulou](mailto:efstathia.christopoulou@huawei.com), [Chenxi Whitehouse](mailto:chenxi.whitehouse@gmail.com)
+## Dataset Description
 We use the English Wikipedia and leverage entity information from Wikidata to construct an entity-based Code Switching corpus.
 To achieve this, we make use of wikilinks in Wikipedia, i.e. links from one page to another.
 If no candidate languages can be found, we do not code-switch the sentence, instead, we keep it as part of the English corpus.
 Finally, we surround each entity with entity indicators (`<e>`, `</e>`).
+## Supported Tasks and Leaderboards
 The dataset was developped for intermediate pre-training of language models.
 In the paper we further fine-tune models on entity-centric downstream tasks, such as NER.
+## Languages
 The dataset covers 93 languages in total, including English.
+## Data Statistics
 | Statistic                     | Count       |
 |:------------------------------|------------:|
 | CS Sentences                  | 231,124,422 |
 | CS Entities                   | 420,907,878 |
+## Data Fields
+Each instance contains 4 fields:
 - `id`: Unique ID of each sentence
 - `language`: The language of choice for entity code-switching of the given sentence
 - `en_sentence`: The original English sentence
 - `cs_sentence`: The code-switched sentence
+In the case of the English subset, the `cs_sentence` field does not exist as the sentences are not code-switched.
 An example of what a data instance looks like:
 ```
 }
 ```
+## Data Splits
 There is a single data split for each language. You can randomly select a few examples from each language to serve as validation set.
+## Limitations
 An important limitation of the work is that before code-switching an entity, its morphological inflection is not checked.
 This can lead to potential errors as the form of the CS entity might not agree with the surrounding context (e.g. plural).
 This choice was for a better comparison between models, however it is possible to extend the corpus with more languages that XLM-R does not cover, following
 the procedure presented in the paper.
+## Citation
+**BibTeX**
 ```html
 @inproceedings{whitehouse-etal-2022-entitycs,
     pages = "6698--6714"
 }
 ```
+**APA**
+```html
+Whitehouse, C., Christopoulou, F., & Iacobacci, I. (2022). EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching. In Findings of the Association for Computational Linguistics: EMNLP 2022.
+```