Dataset Geography: Mapping Language Data to Language Users

Abstract

As language technologies become more ubiquitous, there are increasing efforts towards expanding the language diversity and coverage of natural language processing (NLP) systems. Arguably, the most important factor influencing the quality of modern NLP systems is data availability. In this work, we study the geographical representativeness of NLP datasets, aiming to quantify if and by how much do NLP datasets match the expected needs of the language speakers. In doing so, we use entity recognition and linking systems, also making important observations about their cross-lingual consistency and giving suggestions for more robust evaluation. Last, we explore some geographical and economic factors that may explain the observed dataset distributions.

Publication
Proceedings of the Association for Computational Linguistics: ACL 2022
Fahim Faisal
Fahim Faisal
PhD Student

My name is Fahim Faisal. My academic interest involves learning different aspects of computational linguistics and natural language processing (eg. machine translation). Currently, I am working on a project related to semi-supervised learning of morphological process of language.

Yinkai Wang
Yinkai Wang
Undergraduate → PhD@Tufts
Antonios Anastasopoulos
Antonios Anastasopoulos
Assistant Professor

I work on multilingual models, machine translation, speech recognition, and NLP for under-served languages.

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