Designing a Machine Translation System for Canadian Weather ...

Designing a Weather Warning Translation System 2
transcripts proved not to be appropriate. Instead, we had to harvest parallel text
from sometimes poorly maintained archives. To further complicate the matter, their
bulk was in a form that could not be readily exploited, since properly-cased letters
and diacritics were missing. In contrast, our goal was to produce translations as
close as possible to a properly formatted text. Although the exact processes we
describe are specific to our application, we are convinced that any productionquality
SMT system will have to go through similar unglamorous but nevertheless
essential procedures.
This paper emphasizes the need for the integration of many sources of information
in order to produce a high quality translated text. In this project, we made
good use of our long experience in the area of processing weather information and
we benefited greatly from our close association with the people at Environment
Canada. They kindly agreed to offer rich sources of meteorological information and
they provided continuous feedback on the state of our prototypes. Indeed, too often
MT projects rely solely on automatic evaluation methods such as BLEU scores, but
the real test is human evaluation. It is costly but, as we show, these statistics can
bring a much better insight into the development of a production quality system.
Any “real” SMT system is much more than a mere translation engine; it features
modules to gather source text, to preprocess it, to keep the output in a translation
memory and to produce formatted text appropriate for dissemination. This paper
describes in detail the intricacies of such a system, not often described but nevertheless
essential in a production setting. At each step, we will present the choices
that have been made through a patient examination of the source texts, the translation
models and the outputs. Even though we are well aware that we did not
always consider all alternatives and sometimes had to opt for simplicity because of
time and resource constraints, we are confident that the resulting system is a good
language engineering use-case, given our experience with these corpora.
2 Context of the Application
In the early 1970s, a research group at the Université de Montréal called TAUM
completed a machine translation project called TAUM-MÉTÉO, which has been
called the most successful case for this type of technology (Mitkov (2005), p. 439).
This system was developed for translating weather forecasts issued by Environment
Canada from English to French, a problem that lent itself well to automation,
given the highly repetitive nature of the text and the tediousness of this specific
translation task for humans. TAUM-MÉTÉO relied on dictionary lookup and syntactic
analysis, followed by simple syntactic and morphological generation rules. An
overview of the system can be found in (Isabelle 1987).
TAUM-MÉTÉO and a number of successors (see Chandioux (1988)) were deployed
at Environment Canada. From 1984 to 2004, they have continually translated
English weather forecasts. Translation professionals from the Canadian Translation
Bureau supervised the process and made sure that the occasional spelling
error or other difficulty found in the source text did not prevent the production
of a French forecast. The quality of TAUM-MÉTÉO’s output is considered very