Designing a Machine Translation System for Canadian Weather ...

Designing a Weather Warning Translation System 12
sentences contained at least one error. While preparing the corpus, we also found
unfaithful translations, although they were extremely rare and should be considered
negligible.
Therefore, it does seem that while the translations are of high quality overall, a
significant proportion of sentences contain errors, a fact that should be taken into
account when relying on the corpus for translating.
4.4.2 Inconsistencies in Format and Phrasing
There are some phrasing inconsistencies in the corpus that required manual intervention.
For instance, numbers are usually written with digits, but are sometimes
written using French and English numerals (e.g. “TEN”). Additionally, negative
numbers are usually prefixed with the word “MINUS” but they are sometimes written
with a minus sign in front. Hours presented a similar problem, where the separator
between hours and minutes sometimes changed from a colon to a period for
certain warnings. Units were either spelled in full (“MILLIMETRE”) or using their
corresponding symbol. Floating-point numbers were sometimes found to use the
comma instead of the period to mark the fractional part.
The text format of the MTCNs also proved to be inconsistent depending on
the source we use (see section 4.3). For instance, the apostrophe may or may not
appear in some words (like “AUJOURD’HUI” in French or after elided words like
“D’ORAGES”).
We attempted to normalize the text by including standardization rules in the
tokenizer we wrote for this study. Multiple iterations of the code were necessary as
we received more and more text over time and discovered new problems.
4.4.3 Serialization Differences
When serializing the corpus using the procedure explained in Section 2, we discovered
discrepancies between the serialized tokens produced by each member of a
sentence pair. For instance, the phrase “DE LA GRELE DE LA TAILLE D UNE BALLE
DE GOLF” is associated with the English phrase “45 MM HAIL”. The latter will be
serialized to “ NUM MM HAIL” while the French version will be left untouched.
This serialization disagreement becomes a problem when deserializing a translated
sentence using the procedure explained in Section 4.1. We are then forced to produce
a translation which cannot be deserialized because there is no corresponding
source material.
We initially encountered quite a lot of these discrepancies and solved most of
them by adding rules in the tokenizer. However, we did not solve all problems and
10k sentence pairs could not be serialized and were added verbatim to the bitext
in order to reduce out of vocabulary coverage. Most of them are caused by hail
sizes and sentence alignment errors. We provide further details about the problems
caused by hail sizes in Section 7.1.