Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training a code-switching language model with monolingual data   A lack of code-switching data complicates the training of code-switching (CS)
language models. We propose an approach to train such CS language models on
monolingual data only. By constraining and normalizing the output projection
matrix in RNN-based language models, we bring embeddings of different languages
closer to each other. Numerical and visualization results show that the
proposed approaches remarkably improve the performance of CS language models
trained on monolingual data. The proposed approaches are comparable or even
better than training CS language models with artificially generated CS data. We
additionally use unsupervised bilingual word translation to analyze whether
semantically equivalent words in different languages are mapped together.

18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
7
The categories of this paper are: hep-th