  Cardiac signals, such as the electrocardiogram, convey a significant amount
of information about the health status of a patient which is typically
summarized by a clinician in the form of a clinical report, a cumbersome
process that is prone to errors. To streamline this routine process, we propose
a deep neural network capable of captioning cardiac signals; it receives a
cardiac signal as input and generates a clinical report as output. We extend
this further to generate multilingual reports. To that end, we create and make
publicly available a multilingual clinical report dataset. In the absence of
sufficient labelled data, deep neural networks can benefit from a warm-start,
or pre-training, procedure in which parameters are first learned in an
arbitrary task. We propose such a task in the form of discriminative
multilingual pre-training where tokens from clinical reports are randomly
replaced with those from other languages and the network is tasked with
predicting the language of all tokens. We show that our method performs on par
with state-of-the-art pre-training methods such as MLM, ELECTRA, and MARGE,
while simultaneously generating diverse and plausible clinical reports. We also
demonstrate that multilingual models can outperform their monolingual
counterparts, informally terming this beneficial phenomenon as the blessing of
multilinguality.
