Are Multilingual Models Effective in Code-Switching?

Winata, G. I., Cahyawijaya, Liu, Lin, Madotto, & Fung, P. (2021). Are Multilingual Models Effective in Code-Switching?. Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, 142-153.

Type

Conference paper

Intellectual base

Primary research

Year

2021

Link

https://aclanthology.org/2021.calcs-1.20/

Cited from

linguistics_semiotics

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Code-Switching
  • Simultaneously, modern multilingual NLP models (XLM-R, mBERT)

    This sourceACL. Directly evaluates mBERT and XLM-R on code-switching tasks, treating code-switched data as a legitimate test case for multilingually pretrained models.

Verification

This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.

Registry ID ref:ff35cfdc01a9 · see in the full table