Rule-based Machine translation from English to Finnish

Hurskainen, Arvi; Tiedemann, Jörg

doi:10.18653/v1/w17-4731

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…HY-AH (Raganato et al, 2018;Hurskainen and Tiedemann, 2017) is a rule-based machine translation system, relying on a rule-based dependency parser for English, a hand-crafted translation lexicon (based on dictionary data extracted from parallel corpora by word alignment), various types of transfer rules, and a morphological generator for Finnish.…”

Section: Gtcommentioning

confidence: 99%

See 1 more Smart Citation

Findings of the 2018 Conference on Machine Translation (WMT18)

Bojar¹,

Federmann²,

Fishel³

et al. 2018

Proceedings of the Third Conference on Machine Translation: Shared Task Papers

269

223

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This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test sets to probe specific aspects of translation. Language Sources (Number of Documents) English ABC News (1), BBC (4), Brisbane Times (1), CBS News (1), Daily Mail (4), Euronews (3), Globe and Mail (1), Guardian (4), Independent (4), Los Angeles Times (4), MSNBC (3), Novinte (2), New York Times (2), Reuters (3), Russia Today (2), Scotsman (2), Sydney Morning Herald (2), Telegraph (2), The Local (2), Time Magazine (2), UPI (1), Washington Post (3) Czech blesk.cz (16), deník.cz (5), Deník Referendum (1), DNES.cz (7), lidovky.cz (6), Novinky.cz (3), Reflex (2), tyden.cz (12), ZDN (2) German Aachener Nachrichten (1), Abendzeitung Nürnberg (2), Braunschweiger Zeitung (1

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Section: Gtcommentioning

confidence: 99%

“…The final system is trained with four different seeds and mixed data. (Raganato et al, 2018;Hurskainen and Tiedemann, 2017) The University of Helsinki (HY) submitted four systems: HY-AH, HY-NMT, HY-NMT-2STEP and HY-SMT.…”

Section: Alibaba (mentioning

confidence: 99%

Findings of the 2018 Conference on Machine Translation (WMT18)

Bojar¹,

Federmann²,

Fishel³

et al. 2018

Proceedings of the Third Conference on Machine Translation: Shared Task Papers

269

223

View full text Add to dashboard Cite

show abstract

“…In comparison to neural and statistical systems, the rule-based approach does not generally fare well as measured with automatic metrics like BLEU, for a human evaluation refer to (Bojar et al, 2019). However, the experiment I describe here is also not the most actively developed machine translators, rather I use the experiment to 14 http://matrix.statmt.org/matrix/ output/1903?score_id=39757 gauge the effects the described workflow has to quality of semi-automatically generated RBMT, to see how more developed systems fare on the same task you should also refer to (Hurskainen and Tiedemann, 2017;Kolachina and Ranta, 2015).…”

Section: Evaluation Error Analysis and Discussionmentioning

confidence: 99%

Apertium-fin-eng–Rule-based Shallow Machine Translation for WMT 2019 Shared Task

Pirinen¹

2019

Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

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In this paper I describe a rule-based, bidirectional machine translation system for the Finnish-English language pair. The original system is based on the existing data of FinnWordNet, omorfi and apertium-eng. I have built the disambiguation, lexical selection and translation rules by hand. The dictionaries and rules have been developed based on the shared task data. I describe in this article the use of the shared task data as a kind of a test-driven development workflow in RBMT development and show that it suits perfectly to a modern software engineering continuous integration workflow of RBMT and yields big increases to BLEU scores with minimal effort. The system described in the article is mainly developed during shared tasks.

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“…Tiedemann et al 2015;Pirinen et al 2016), NMT (e.g. Östling et al 2017;Grönroos et al 2017) and RBMT (Hurskainen and Tiedemann 2017) systems have been developed in academic settings. Finnish is included in online systems like Google Translate, and some commercial RBMT systems exist for the language pairs English-Finnish-English (Sunda) and Finnish-English (TranSmart), and reports from the field also indicate integration of proprietary (S)MT systems by some translation service providers like Lingsoft (see Ervasti 2017).…”

Section: Nmt Output and Qualitymentioning

confidence: 99%

A product and process analysis of post-editor corrections on neural, statistical and rule-based machine translation output

Koponen

Salmi

Nikulin

2019

Machine Translation

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This paper presents a comparison of post-editing (PE) changes performed on English-to-Finnish neural (NMT), rule-based (RBMT) and statistical machine translation (SMT) output, combining a product-based and a process-based approach. A total of 33 translation students acted as participants in a PE experiment providing both post-edited texts and edit process data. Our product-based analysis of the postedited texts shows statistically significant differences in the distribution of edit types between machine translation systems. Deletions were the most common edit type for the RBMT, insertions for the SMT, and word form changes as well as word substitutions for the NMT system. The results also show significant differences in the correctness and necessity of the edits, particularly in the form of a large number of unnecessary edits in the RBMT output. Problems related to certain verb forms and ambiguity were observed for NMT and SMT, while RBMT was more likely to handle them correctly. Process-based comparison of effort indicators shows a slight increase of keystrokes per word for NMT output, and a slight decrease in average pause length for NMT compared to RBMT and SMT in specific text blocks. A statistically significant difference was observed in the number of visits per sub-segment, which is lower for NMT than for RBMT and SMT. The results suggest that although different types of edits were needed to outputs from NMT, RBMT and SMT systems, the difference is not necessarily reflected in process-based effort indicators.

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Rule-based Machine translation from English to Finnish

Abstract: The paper describes a rule-based machine translation system adapted to English to Finnish translation. Although the translation system participates in the shared task of news translation in WMT 2017, the paper describes the strengths and weaknesses of the approach in general.

Cited by 16 publications

References 9 publications

Findings of the 2018 Conference on Machine Translation (WMT18)

Findings of the 2018 Conference on Machine Translation (WMT18)

Apertium-fin-eng–Rule-based Shallow Machine Translation for WMT 2019 Shared Task

A product and process analysis of post-editor corrections on neural, statistical and rule-based machine translation output

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