Abstract
This work focuses on building language models (LMs) for code-switched text. We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately 2) Pretraining the LM using synthetic text from a generative model estimated using the training data. We demonstrate the effectiveness of our proposed techniques by reporting perplexities on a Mandarin-English task and derive significant reductions in perplexity.- Anthology ID:
- D18-1346
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3078–3083
- Language:
- URL:
- https://aclanthology.org/D18-1346
- DOI:
- 10.18653/v1/D18-1346
- Cite (ACL):
- Saurabh Garg, Tanmay Parekh, and Preethi Jyothi. 2018. Code-switched Language Models Using Dual RNNs and Same-Source Pretraining. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3078–3083, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Code-switched Language Models Using Dual RNNs and Same-Source Pretraining (Garg et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D18-1346.pdf