Normal view MARC view ISBD view

Statistical machine translation of croatian weather forecasts: how much data do we need? / Ljubešić, Nikola ; Bago, Petra ; Boras, Damir.

By: Ljubešić, Nikola, informatičar.
Contributor(s): Boras, Damir [aut] | Bago, Petra [aut].
Material type: materialTypeLabelArticleDescription: 303-308.ISSN: 1330-1136.Other title: Statistical Machine Translation of Croatian Weather Forecasts: How Much Data Do We Need? [Naslov na engleskom:].Subject(s): 5.04 | statistical machine translation, weather forecast, automatic evaluation, human evaluation hrv | statistical machine translation, weather forecast, automatic evaluation, human evaluation engOnline resources: Click here to access online In: CIT. Journal of computing and information technology 18 (2010), 4 ; str. 303-308Summary: This research is the first step towards developing a system for translating Croatian weather forecasts into multiple languages. This step deals with the Croatian-English language pair. The parallel corpus consists of a one-year sample of the weather forecasts for the Adriatic, con- sisting of 7, 893 sentence pairs. Evaluation is performed by the automatic evaluation measures BLUE, NIST and METEOR, as well as by manually evaluating a sample of 200 translations. We have shown that with a small- sized training set and the state-of-the art Moses system, decod- ing can be done with 96% accuracy concerning adequacy and fluency. Additional improvement is expected by increasing the training set size. Finally, the correlation of the recorded evaluation measures is explored.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

This research is the first step towards developing a system for translating Croatian weather forecasts into multiple languages. This step deals with the Croatian-English language pair. The parallel corpus consists of a one-year sample of the weather forecasts for the Adriatic, con- sisting of 7, 893 sentence pairs. Evaluation is performed by the automatic evaluation measures BLUE, NIST and METEOR, as well as by manually evaluating a sample of 200 translations. We have shown that with a small- sized training set and the state-of-the art Moses system, decod- ing can be done with 96% accuracy concerning adequacy and fluency. Additional improvement is expected by increasing the training set size. Finally, the correlation of the recorded evaluation measures is explored.

Projekt MZOS 130-1301679-1380

ENG

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha