GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models

Da Yin, Hritik Bansal, Masoud Monajatipoor, Liunian Harold Li, Kai-Wei Chang


Abstract
Recent work has shown that Pre-trained Language Models (PLMs) store the relational knowledge learned from data and utilize it for performing downstream tasks. However, commonsense knowledge across different regions may vary. For instance, the color of bridal dress is white in American weddings whereas it is red in Chinese weddings. In this paper, we introduce a benchmark dataset, Geo-diverse Commonsense Multilingual Language Models Analysis (GeoMLAMA), for probing the diversity of the relational knowledge in multilingual PLMs. GeoMLAMA contains 3125 prompts in English, Chinese, Hindi, Persian, and Swahili, with a wide coverage of concepts shared by people from American, Chinese, Indian, Iranian and Kenyan cultures. We benchmark 11 standard multilingual PLMs on GeoMLAMA. Interestingly, we find that 1) larger multilingual PLMs variants do not necessarily store geo-diverse concepts better than its smaller variant; 2) multilingual PLMs are not intrinsically biased towards knowledge from the Western countries (the United States); 3) the native language of a country may not be the best language to probe its knowledge and 4) a language may better probe knowledge about a non-native country than its native country.
Anthology ID:
2022.emnlp-main.132
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
Note:
Pages:
2039–2055
Language:
URL:
https://aclanthology.org/2022.emnlp-main.132
DOI:
Bibkey:
Cite (ACL):
Da Yin, Hritik Bansal, Masoud Monajatipoor, Liunian Harold Li, and Kai-Wei Chang. 2022. GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2039–2055, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models (Yin et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.132.pdf