Datasets:

huawei-noah
/

entity_cs

 ---
 license: apache-2.0
+language:
+- af
+- am
+- ar
+- as
+- az
+- be
+- bg
+- bn
+- br
+- bs
+- ca
+- cs
+- cy
+- da
+- de
+- en
+- el
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- fy
+- ga
+- gd
+- gl
+- gu
+- ha
+- he
+- hi
+- hr
+- hu
+- hy
+- id
+- is
+- it
+- ja
+- jv
+- ka
+- kk
+- km
+- kn
+- ko
+- ku
+- ky
+- la
+- lo
+- lt
+- lv
+- mg
+- mk
+- ml
+- mn
+- mr
+- ms
+- my
+- ne
+- nl
+- nb
+- om
+- or
+- pa
+- pl
+- ps
+- pt
+- ro
+- ru
+- sa
+- sd
+- si
+- sk
+- sl
+- so
+- sq
+- sr
+- su
+- sv
+- sw
+- ta
+- te
+- th
+- tl
+- tr
+- ug
+- uk
+- ur
+- uz
+- vi
+- xh
+- yi
+- zh
+size_categories:
+- 100M<n<1B
 ---
+# 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: [email protected]
+### 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.
+We use the English [Wikipedia dump](https://dumps.wikimedia.org/enwiki/latest/) (November 2021) and extract raw text with [WikiExtractor](https://github.com/attardi/wikiextractor) while keeping track of wikilinks.
+Since we are interested in creating entity-level CS instances, we only keep sentences containing at least one wikilink.
+Given an English sentence with wikilinks, we first map the entity in each wikilink to its corresponding Wikidata ID and
+retrieve its available translations from Wikidata.
+For each sentence, we check which languages have translations for all entities in that sentence, and consider those as candidates for code-switching.
+We ensure all entities are code-switched to the same target language in a single sentence, avoiding noise from including too many languages.
+To control the size of the corpus, we generate up to five code-switched sentences for each English sentence.
+In particular, if fewer than five languages have translations available for all the entities in a sentence, we create code-switched instances with all of them.
+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 and can be used on any downstream task.
+In the paper it's effectiveness is proven on entity-centric tasks, such as NER.
+### Languages
+The dataset covers 93 languages in total, including English.
+## Dataset Structure
+### Data Statistics
+| Statistic                     | Count       |
+|:------------------------------|------------:|
+| Languages                     | 93          |
+| English Sentences             | 54,469,214  |
+| English Entities              | 104,593,076 |
+| Average Sentence Length       | 23.37       |
+| Average Entities per Sentence | 2           |
+| CS Sentences per EN Sentence  | ≤ 5         |
+| 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
+An example of what a data instance looks like:
+```
+{
+  'id': 19,
+  'en_sentence': 'The subs then enter a <en>coral reef</en> with many bright reflective colors.',
+  'cs_sentence': 'The subs then enter a <de>Korallenriff</de> with many bright reflective colors.',
+  'language': 'de'
+}
+```
+### Data Splits
+There is a single data split for each language. You can randomly select a few examples 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).
+There should be few cases as such, as we are only switching entities. However, this should be improved in a later version of the corpus.
+Secondly, the diversity of languages used to construct the EntityCS corpus is restricted to the overlap between the available languages in WikiData and XLM-R pre-training.
+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,
+    title = "{E}ntity{CS}: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching",
+    author = "Whitehouse, Chenxi  and
+      Christopoulou, Fenia  and
+      Iacobacci, Ignacio",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-emnlp.499",
+    pages = "6698--6714"
+}
+```