Paper: Learning to Predict Code-Switching Points

ACL ID D08-1102
Title Learning to Predict Code-Switching Points
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

Predicting possible code-switching points can help develop more accurate methods for au- tomatically processing mixed-language text, such as multilingual language models for speech recognition systems and syntactic an- alyzers. We present in this paper exploratory results on learning to predict potential code- switching points in Spanish-English. We trained different learning algorithms using a transcription of code-switched discourse. To evaluate the performance of the classifiers, we used two different criteria: 1) measuring pre- cision, recall, and F-measure of the predic- tions against the reference in the transcrip- tion, and 2) rating the naturalness of artifi- cially generated code-switched sentences. Av- erage scores for the code-switched sentences generated by our machine learning...