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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning

The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the creation of XCOPA and the implementation of the baselines are available in the paper.

Languages | Baselines | Cite | Paper

Data

The XCOPA data are stored in data. Alternatively, they can be loaded through 🤗Datasets as follows:

from datasets import load_dataset

xcopa_dataset = load_dataset('xcopa')

The "translate test" data obtained via Google Translate are available in the folder data-gmt (note that these do not include Quechua).

Examples

Language	Premise	Question	Choice 1	Choice 2
qu	Sipasqa cereal mikhunanpi kuruta tarirqan.	Result	Payqa pukunman ñuqñuta churakurqan.	Payqa manam mikhuyta munarqanchu.
en	The girl found a bug in her cereal.	Result	She poured milk in the bowl.	She lost her appetite.
th	ตาของฉันแดงและบวม	Cause	ฉันร้องไห้	ฉันหัวเราะ
en	My eyes became red and puffy.	Cause	I was sobbing.	I was laughing.

Languages

ISO 639-2	Name	Family	Area<sup>1</sup>
et	Estonian	Uralic	Northern Europe
ht	Haitian Creole	French Creole	Carribean
id	Indonesian	Austronesian	Southeastern Asia
it	Italian	Indo-European	Southern Europe
qu	Southern Quechua<sup>2</sup>	Quechuan	Southern America
sw	Swahili	Niger-Congo	Eastern Africa
ta	Tamil	Dravidian	Southern Asia
th	Thai	Kra-Dai	Southeastern Asia
tr	Turkish	Turkic	Western Asia
vi	Vietnamese	Austroasiatic	Southeastern Asia
zh	Mandarin Chinese	Sino-Tibetan	Eastern Asia

<sup>1</sup> According to the United Nations geoscheme.

<sup>2</sup> Translation by Irma Alvarez Ccoscco, an Eastern Apurímac Quechua speaker.

Leaderboard

If you want to see your results reported, please:

submit them on the XCOPA state-of-the-art page on Papers with Code;
make a pull request changing the table below.

Model	Paper	avg	et	ht	id	it	qu	sw	ta	th	tr	vi	zh
Human	Ponti et al. (2020)	97.60	98.2	96.4	100.0	97.0	94.8	99.0	98.6	98.2	96.4	98.4	96.6
RoBERTa Large (Translate test)	Ponti et al. (2020)	76.05	81.0	73.8	82.2	77.8	(50.0)	74.2	79.6	71.4	79.6	81.0	86.0
XLM-R Large	Ponti et al. (2020)	68.69	71.4	(50.0)	79.8	72.6	(50.0)	59.2	73.0	72.8	74.4	73.8	78.6
MAD-X Base	Pfeiffer et al. (2020)	60.94	61.3	53.7	65.8	63.0	52.5	56.3	61.9	61.8	60.3	66.1	67.6

The performance of other multilingual pre-trained encoders is shown in the figure.

Cite

If you use the data from this repository, please cite both XCOPA \cite{ponti2020xcopa} and the original COPA paper \cite{roemmele2011choice}.

@inproceedings{ponti2020xcopa,
  title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning},
  author={Edoardo M. Ponti, Goran Glava\v{s}, Olga Majewska, Qianchu Liu, Ivan Vuli\'{c} and Anna Korhonen},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2020},
  url={https://ducdauge.github.io/files/xcopa.pdf}
}

@inproceedings{roemmele2011choice,
  title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
  author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
  booktitle={2011 AAAI Spring Symposium Series},
  year={2011},
  url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF},
}