Abstract
Code-mixing (CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. There are no strict grammatical constraints observed in code-mixing, and it consists of non-standard variations of spelling. The linguistic complexity resulting from the above factors made the computational analysis of the code-mixed language a challenging task. Language identification (LI) and part of speech (POS) tagging are the fundamental steps that help analyze the structure of the code-mixed text. Often, the LI and POS tagging tasks are interdependent in the code-mixing scenario. We project the problem of dealing with multilingualism and grammatical structure while analyzing the code-mixed sentence as a joint learning task. In this paper, we jointly train and optimize language detection and part of speech tagging models in the code-mixed scenario. We used a Transformer with convolutional neural network architecture. We train a joint learning method by combining POS tagging and LI models on code-mixed social media text obtained from the ICON shared task.- Anthology ID:
- 2021.ranlp-1.42
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 367–374
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.42
- DOI:
- Cite (ACL):
- Suman Dowlagar and Radhika Mamidi. 2021. A Pre-trained Transformer and CNN Model with Joint Language ID and Part-of-Speech Tagging for Code-Mixed Social-Media Text. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 367–374, Held Online. INCOMA Ltd..
- Cite (Informal):
- A Pre-trained Transformer and CNN Model with Joint Language ID and Part-of-Speech Tagging for Code-Mixed Social-Media Text (Dowlagar & Mamidi, RANLP 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2021.ranlp-1.42.pdf