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Record W1557439938

Shallow-Transfer Rule-Based Machine Translation between Icelandic and Swedish. Developing Apertium-is-sv: A Bidirectional Open-Source RBMT Application for Icelandic and Swedish

2013· article· en· W1557439938 on OpenAlexfundno aff
Tihomir Rangelov

Bibliographic record

VenueSkemman · 2013
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
FundersAlberta-Pacific Forest Industries
KeywordsIcelandicTranslation (biology)Computer scienceOpen sourceArtificial intelligenceChemistryLinguisticsSoftware
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the development of Apertium-is-sv, a bidirectional shallow- transfer rule-based machine translation application for Icelandic and Swedish. The system was implemented in the open-source RBMT platform Apertium and was developed according to its standards.
\nThe development of the system included the creation of a bilingual dictionary, two monolingual dictionaries, one for each language, syntactic transfer rules and the first steps towards the development of open-source constraint grammars for both Swedish and Icelandic.
\nThe evaluation of the system showed that its performance was in line with other Apertium pairs and very similar to that of the other available MT tool for this language pair, Google Translate. In order to improve its performance, more work will need to be done on all modules that comprise the system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.275
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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