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Designing a Weather Warning Translation System 34 Table 11. A source sentence, WATT’s translation and four different revisions sets, by four different reviewers. The number of changes made appears in parentheses after the reviewer’s identifier. Insertions are indicated by a double underline and the deletions by struck-out text. Source WATT T1 (6) T2 (6) M1 (7) M2 (2) CE SYSTEME FORTEMENT CHARGE EN HUMIDITE RENCONTRERA L’AIR FROID EN PROVENANCE DU NORD DE LA PROVINCE DE TELLE SORTE QUE DE LA NEIGE PARFOIS FORTE AFFECTERA UN AXE ENTRE LE TEMISCAMINGUE ET CHIBOUGAMAU A COMPTER DE CE SOIR. This system strongly charged with humidity will meet the cold air from the north of the province and the snow at times heavy will affect a line between Témiscamingue and Chibougamau beginning this evening. THIS MOISTURE LADEN SYSTEM STRONGLY CHARGED WITH HUMIDITY WILL MEET THE COLD AIR FROM THE NORTH OF THE PROVINCE AND THENORTHERN QUEBEC SO THAT SNOW AT TIMES HEAVY WILL AFFECT REGIONS ON A LINE BETWEEN TEMISCAMINGUE AND CHIBOUGAMAU BEGINNING THIS EVENING. THIS SYSTEM STRONGLY CHARGEDLADEN WITH HUMIDITY WILL MEET THE COLD AIR COMING FROM THE NORTH OF THE PROVINCE AND THEWILL PRODUCE SNOW AT TIMES HEAVY; SNOW WILL AFFECT A LINE BETWEEN TEMISCAMINGUE AND CHIBOUGAMAU BEGINNING THIS EVENING. This very moist system strongly charged with humidity will meet the cold air from the north of the province and the, causing snow at times heavy will affect along a line between Témiscamingue and Chibougamau, beginning this evening. This system strongly charged with, containing humidity will meet the cold air from the north of the province and the snow at times heavy will affect a line between Témiscamingue and Chibougamau beginning this evening. such a policy, and did prove extremely useful when parsing some of the resources used in this study. Secondly, all efforts should be made to gather as much data as possible in order to create a technology such as the one we described here. Most notably, this means an archive of relevant documents should be maintained and statistics on these archives should be available. This is a problem we repeatedly encountered here when we attempted to collect historical translations. When using example-based, statistical, or other corpus-based approaches, widespread errors or numerous mistranslations in the train data risk creeping up in the machine translation. Investing a reasonable amount of effort in sanitizing this data can be very beneficial, but it is always much easier if these mistakes are avoided
Designing a Weather Warning Translation System 35 in the first place, when the text is written. In our case, we observed typographical errors in the source and target texts, which diminished the quality of the machine output. These typos are always expected in such a great quantity of text, but we feel that some of them could have been avoided. For instance, a spellchecker could have been used when creating the original bulletin, or when the translation was produced. However, this could prove tricky in some cases, because these discussions contain many place names and terms belonging to the meteorological jargon. The various word lists, vocabularies and gazetteers that we have compiled in this study could help overcome this problem. Another potentially interesting tool to raise the quality of the bilingual warning corpus at the source could be a computer-aided bulletin creation tool, meant to help the meteorologist. Such a system would filter out misspelled or unknown words, standardize the notation and the vocabulary, and would ultimately ameliorate the source text and its translations. Evaluations both automatic and manual are also essential to provide not only the necessary tuning parameters for the translation engine but also to get new insight into the overall performance. Humans are easily badly influenced by seemingly simple errors in formatting or spelling. We thus feel that a good mix of linguistics, patience and engineering is essential for the development of a successful machine translation system. 10 Acknowledgments We would like to thank Michel Jean, Gaétan Deaudelin and Jennifer Milton from Environment Canada for being so supportive of our machine translation efforts over the years. We want to thank also Elliott Macklovitch who managed the third evaluation at the Translation Bureau of Canada and many other evaluators who annotated translations at various stages in our development. We would like to thank the reviewers for their thoughtful comments. This work was supported in part by a contribution from Environment Canada and by a matching grant from Mitacs/Mprime. References Bertoldi, N., Haddow, B. and Fouet, J.-B. (2009) Improved Minimum Error Rate Training in Moses. The Prague Bulletin of Mathematical Linguistics, 91: 7–16. Chandioux, J. (1988) meteo: An Operational Translation System. Proceedings of the 2nd Conference on RIAO, Cambridge, Massachusetts, pp. 829–839. Chen, S. F. and Goodman, J. (1999) An Empirical Study of Smoothing Techniques for Language Modeling. Computer Speech and Language, Elsevier, 13(4): 359–393. Clark, J. H., Dyer, C., Lavie, A. and Smith, N. A. (2011) Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Stroudsburg, PA, pp. 176–181. Foster, G., Kuhn, R. and Johnson, J. H. (2006) Phrasetable Smoothing for Statistical Machine Translation. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, pp. 53–61.
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