  Neural language modeling (LM) has led to significant improvements in several
applications, including Automatic Speech Recognition. However, they typically
require large amounts of training data, which is not available for many domains
and languages. In this study, we propose a multilingual neural language model
architecture, trained jointly on the domain-specific data of several
low-resource languages. The proposed multilingual LM consists of language
specific word embeddings in the encoder and decoder, and one language specific
LSTM layer, plus two LSTM layers with shared parameters across the languages.
This multilingual LM model facilitates transfer learning across the languages,
acting as an extra regularizer in very low-resource scenarios. We integrate our
proposed multilingual approach with a state-of-the-art highly-regularized
neural LM, and evaluate on the conversational data domain for four languages
over a range of training data sizes. Compared to monolingual LMs, the results
show significant improvements of our proposed multilingual LM when the amount
of available training data is limited, indicating the advantages of
cross-lingual parameter sharing in very low-resource language modeling.
