  Back-translation (BT) of target monolingual corpora is a widely used data
augmentation strategy for neural machine translation (NMT), especially for
low-resource language pairs. To improve effectiveness of the available BT data,
we introduce HintedBT -- a family of techniques which provides hints (through
tags) to the encoder and decoder. First, we propose a novel method of using
both high and low quality BT data by providing hints (as source tags on the
encoder) to the model about the quality of each source-target pair. We don't
filter out low quality data but instead show that these hints enable the model
to learn effectively from noisy data. Second, we address the problem of
predicting whether a source token needs to be translated or transliterated to
the target language, which is common in cross-script translation tasks (i.e.,
where source and target do not share the written script). For such cases, we
propose training the model with additional hints (as target tags on the
decoder) that provide information about the operation required on the source
(translation or both translation and transliteration). We conduct experiments
and detailed analyses on standard WMT benchmarks for three cross-script
low/medium-resource language pairs: {Hindi,Gujarati,Tamil}-to-English. Our
methods compare favorably with five strong and well established baselines. We
show that using these hints, both separately and together, significantly
improves translation quality and leads to state-of-the-art performance in all
three language pairs in corresponding bilingual settings.
