Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media. For instance, people of distinct cultures often hold different opinions on a single named entity. Also, understanding slang terms across languages requires knowledge of cross-cultural similarities. In this paper, we study the problem of computing such cross-cultural differences and similarities. We present a lightweight yet effective approach, and evaluate it on two novel tasks: 1) mining cross-cultural differences of named entities and 2) finding similar terms for slang across languages. Experimental results show that our framework substantially outperforms a number of baseline methods on both tasks. The framework could be useful for machine translation applications and research in computational social science.
|Title of host publication||ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||11|
|Publication status||Published - 2018|
|Event||56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia|
Duration: 2018 Jul 15 → 2018 Jul 20
|Name||ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)|
|Conference||56th Annual Meeting of the Association for Computational Linguistics, ACL 2018|
|Period||18/7/15 → 18/7/20|
Bibliographical noteFunding Information:
Kenny Zhu is the contact author and was supported by NSFC grants 91646205 and 61373031. Seung-won Hwang was supported by Microsoft Research Asia. Thanks to the anonymous reviewers and Hanyuan Shi for their valuable feedback.
© 2018 Association for Computational Linguistics
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics