Challenges and Strategies in Cross-Cultural NLP

by   Daniel Hershcovich, et al.

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.


page 1

page 2

page 3

page 4


Dataset Geography: Mapping Language Data to Language Users

As language technologies become more ubiquitous, there are increasing ef...

EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English

While cultural backgrounds have been shown to affect linguistic expressi...

Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

Parliamentary and legislative debate transcripts provide an exciting ins...

Evaluating Language Tools for Fifteen EU-official Under-resourced Languages

This article presents the results of the evaluation campaign of language...

Bootstrapping NLP tools across low-resourced African languages: an overview and prospects

Computing and Internet access are substantially growing markets in South...

Is Machine Learning Speaking my Language? A Critical Look at the NLP-Pipeline Across 8 Human Languages

Natural Language Processing (NLP) is increasingly used as a key ingredie...

An Inclusive Notion of Text

Natural language processing researchers develop models of grammar, meani...

Please sign up or login with your details

Forgot password? Click here to reset