Mining Cross-Cultural Differences and Similarities in Social Media

Lin, Bill Yuchen; Xu, Frank F.; Zhu, Kenny Q.; Hwang, Seung Won

doi:10.18653/v1/p18-1066

Cited by 20 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quantifying Cross-cultural Similarity A few recent work in psycholinguistics and NLP have aimed to measure cultural differences, mainly from word-level semantics. Lin et al (2018) suggested a cross-lingual word alignment method that preserves the cultural, social context of words. They derive cross-cultural similarity from the embeddings of a bilingual lexicon in the shared representation space.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

Sun¹,

Ahn²,

Park³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

View full text Add to dashboard Cite

Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages. We hypothesize that these measures of linguistic proximity are not enough when working with pragmaticallymotivated tasks, such as sentiment analysis. As an alternative, we introduce three linguistic features that capture cross-cultural similarities that manifest in linguistic patterns and quantify distinct aspects of language pragmatics: language context-level, figurative language, and the lexification of emotion concepts. Our analyses show that the proposed pragmatic features do capture cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology. We further corroborate the effectiveness of pragmatically-driven transfer in the downstream task of choosing transfer languages for cross-lingual sentiment analysis.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Quantifying cross-cultural similarities from linguistic patterns has largely been unexplored in NLP, with the exception of studies that focused on cross-cultural differences in word usage (Garimella et al, 2016;Lin et al, 2018). In this work, we aim to quantify cross-cultural similarity, focusing *The first three authors contributed equally.…”

mentioning

confidence: 99%

Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

Sun¹,

Ahn²,

Park³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

View full text Add to dashboard Cite

show abstract

“…As shown in the bottom-left histogram of Fig. 5, half of the correctly predicted facts were correct in a single language, indicating little overlap across languages (Lin et al, 2018). Only 3% of facts were correct in more than 5 languages, and objects in those facts are usually sub-strings of subjects, making them easy to retrieve regardless of the language.…”

Section: Improving Multilingual Lm Retrievalmentioning

confidence: 96%

X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models

Jiang¹,

Anastasopoulos²,

Araki³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

View full text Add to dashboard Cite

Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-theblank questions such as "Punta Cana is located in _." However, while knowledge is both written and queried in many languages, studies on LMs' factual representation ability have almost invariably been performed on English. To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for 23 typologically diverse languages. To properly handle language variations, we expand probing methods from single-to multi-word entities, and develop several decoding algorithms to generate multi-token predictions. Extensive experimental results provide insights about how well (or poorly) current state-of-theart LMs perform at this task in languages with more or fewer available resources. We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages. Benchmark data and code have be released at https: //x-factr.github.io.

show abstract

“…Gutiérrez et al (2016) detect differences of word usage in the cross-lingual topics of multilingual topic modeling results. Lin et al (2018) present distributional approaches to compute cross-cultural differences or similarities between two terms from different cultures focusing primarily on named entities. Our work is not limited to word usage or any particular topics.…”

Section: Cultural Difference In Word Usagementioning

confidence: 99%

Identifying Distributional Perspectives from Colingual Groups

Tian¹,

Chakrabarty²,

Peng³

2021

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

View full text Add to dashboard Cite

Perspective differences exist among different cultures or languages. A lack of mutual understanding among different groups about their perspectives on specific values or events may lead to uninformed decisions or biased opinions. Automatically understanding the group perspectives can provide essential background for many downstream applications of natural language processing techniques. In this paper, we study colingual groups 1 and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. On a held out set of diverse topics including marriage, corruption, democracy, our model achieves high correlation with human judgements regarding intra-group values and inter-group differences.

show abstract

Mining Cross-Cultural Differences and Similarities in Social Media

Cited by 20 publications

References 27 publications

Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models

Identifying Distributional Perspectives from Colingual Groups

Contact Info

Product

Resources

About