Multilingual Semantic Parsing And Code-Switching
Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip R. Cohen, Mark S. Johnson · 2017
Extending semantic parsing systems to new domains and languages is a highly expensive, time-consuming process, so making effective use of existing resources is critical.In this paper, we describe a transfer learning method using crosslingual word embeddings in a sequence-tosequence model.On the NLmaps corpus, our approach achieves state-of-the-art accuracy of 85.7% for English.Most importantly, we observed a consistent improvement for German compared with several baseline domain adaptation techniques.As a by-product of this approach, our models that are trained on a combination of English and German utterances perform reasonably well on codeswitching utterances which contain a mixture of English and German, even though the training data does not contain any code-switching.As far as we know, this is the first study of code-switching in semantic parsing.We manually constructed the set of code-switching test utterances for the NLmaps corpus and achieve 78.3% accuracy on this dataset.