mlsanityData to 14 Oct 2026

Code-switched Language Models Using Dual RNNs and Same-Source Pretraining

arXiv 1809.01962 ↗First posted 6 Sep 2018cs.CL cs.LGcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2018.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 4; 2024: 1; 2025: 3; 2026: 1.

16 papers.

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