Abstract
Code-switching occurs when more than one language is mixed in a given sentence or a conversation. This phenomenon is more prominent on social media platforms and its adoption is increasing over time. Therefore code-mixed NLP has been extensively studied in the literature. As pre-trained transformer-based architectures are gaining popularity, we observe that real code-mixing data are scarce to pre-train large language models. We present L3Cube-HingCorpus, the first large-scale real Hindi-English code mixed data in a Roman script. It consists of 52.93M sentences and 1.04B tokens, scraped from Twitter. We further present HingBERT, HingMBERT, HingRoBERTa, and HingGPT. The BERT models have been pre-trained on codemixed HingCorpus using masked language modelling objectives. We show the effectiveness of these BERT models on the subsequent downstream tasks like code-mixed sentiment analysis, POS tagging, NER, and LID from the GLUECoS benchmark. The HingGPT is a GPT2 based generative transformer model capable of generating full tweets. Our models show significant improvements over currently available models pre-trained on multiple languages and synthetic code-mixed datasets. We also release L3Cube-HingLID Corpus, the largest code-mixed Hindi-English language identification(LID) dataset and HingBERT-LID, a production-quality LID model to facilitate capturing of more code-mixed data using the process outlined in this work. The dataset and models are available at https://github.com/l3cube-pune/code-mixed-nlp.- Anthology ID:
- 2022.wildre-1.2
- Volume:
- Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- WILDRE
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 7–12
- Language:
- URL:
- https://aclanthology.org/2022.wildre-1.2
- DOI:
- Cite (ACL):
- Ravindra Nayak and Raviraj Joshi. 2022. L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models. In Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference, pages 7–12, Marseille, France. European Language Resources Association.
- Cite (Informal):
- L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models (Nayak & Joshi, WILDRE 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.wildre-1.2.pdf
- Code
- l3cube-pune/code-mixed-nlp