# Syntactic and Semantic Features For Code-Switching Factored Language Models

arXiv: https://arxiv.org/abs/1710.01809 · HTML: https://mlsanity.com/paper/1710.01809/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2017.

## Built on by (most cited first, 8 of 8)

- [From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text](https://mlsanity.com/paper/2107.06483/) (2021)
- [Layer Collaboration in the Forward-Forward Algorithm](https://mlsanity.com/paper/2305.12393/) (2023)
- [TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline](https://mlsanity.com/paper/2206.13135/) (2022)
- [Code Switched and Code Mixed Speech Recognition for Indic languages](https://mlsanity.com/paper/2203.16578/) (2022)
- [Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching](https://mlsanity.com/paper/2109.00921/) (2021)
- [Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge](https://mlsanity.com/paper/2210.06091/) (2022)
- [elBERto: Self-supervised Commonsense Learning for Question Answering](https://mlsanity.com/paper/2203.09424/) (2022)
- [Methods of Automatic Matrix Language Determination for Code-Switched Speech](https://mlsanity.com/paper/2410.02521/) (2024)

## Abstract (arXiv)

This paper presents our latest investigations on different features for factored language models for Code-Switching speech and their effect on automatic speech recognition (ASR) performance. We focus on syntactic and semantic features which can be extracted from Code-Switching text data and integrate them into factored language models. Different possible factors, such as words, part-of-speech tags, Brown word clusters, open class words and clusters of open class word embeddings are explored. The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model containing Brown word clusters and part-of-speech tags and the model also including clusters of open class word embeddings yield the best mixed error rate results. In summary, the best language model can significantly reduce the perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed error rate by up to 3.4% relative.