  The integration of language models for neural machine translation has been
extensively studied in the past. It has been shown that an external language
model, trained on additional target-side monolingual data, can help improve
translation quality. However, there has always been the assumption that the
translation model also learns an implicit target-side language model during
training, which interferes with the external language model at decoding time.
Recently, some works on automatic speech recognition have demonstrated that, if
the implicit language model is neutralized in decoding, further improvements
can be gained when integrating an external language model. In this work, we
transfer this concept to the task of machine translation and compare with the
most prominent way of including additional monolingual data - namely
back-translation. We find that accounting for the implicit language model
significantly boosts the performance of language model fusion, although this
approach is still outperformed by back-translation.
