Speeding Up Target-Language Driven Part-of-Speech Tagger Training for Machine Translation

Sánchez-Martínez, Felipe; Pérez-Ortiz, Juan Antonio; Forcada, Mikel L.

doi:10.1007/11925231_81

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…• Un desambiguador morfològic, el qual de la seqüència de possibles anàlisis tria la més probable. Aquest mòdul es basa o bé en el model ocult de Markov de primer nivell (Cutting et al, 1992;Sánchez-Martínez et al, 2006) o bé amb una combinació d'aquest amb Constraint Grammar (Bick & Didriksen, 2015).…”

Section: Canonadaunclassified

Una eina per a una llengua en procés d'estandardització: el traductor automàtic català-sard

Fronteddu¹,

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Tyers

2018

Linguamática

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Proposta rebuda el 15 d'octubre 2017 i acceptada per a publicació el 3 de desembre 2017. Una eina per a una llengua en procés d'estandardització: el traductor automàtic català-sardMachine translation from Catalan to Sardinian: a translation tool for a language in the process of standardisation Resum Aquest article presenta el desenvolupament d'un sistema de traducció automàtica en codi obert basat en regles del català al sard mitjançant la plataforma Apertium, parant una atenció especial a la creació del diccionari bilingüe i de les regles de selecció lèxica i transferència estructural. Es mostren alguns problemes derivats de l'estat actual del sard estàndard. S'ha obtingut una tassa d'error per paraula (WER) del 20,5% i una tassa d'error per paraula independent de la posició (PER) del 13,9%. Mitjançant l'anàlisi qualitativa de la traducció de quatre articles enciclopèdics, s'analitzen les causes d'aquests resultats. Paraules clausard, català, traducció automàtica, estandardització lingüística, Apertium, RBMT AbstractThis article describes the development of a free/open-source rule-based machine translation system for Catalan to Sardinian based on the Apertium platform. Special attention is given to the components of the system related with transfer (structural and lexical) and lexical selection, drawing attention to issues stemming from the current state of the Sardinian written norm. The system has a word-error rate (WER) of 20.5% and a position-independent worderror rate (PER) of 13.9%. We analyse the remaining errors by doing a qualitative analysis of the translation of four articles from the encyclopaedic domain.

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Section: Canonadaunclassified

Una eina per a una llengua en procés d'estandardització: el traductor automàtic català-sard

Fronteddu¹,

Font

Tyers

2018

Linguamática

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Using target-language information to train part-of-speech taggers for machine translation

Sánchez-Martínez

Pérez-Ortiz

Forcada

2008

Machine Translation

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Although corpus-based approaches to machine translation (MT) are growing in interest, they are not applicable when the translation involves lessresourced language pairs for which there are no parallel corpora available; in those cases, the rule-based approach is the only applicable solution. Most rule-based MT systems make use of part-of-speech (PoS) taggers to solve the PoS ambiguities in the source-language texts to translate; those MT systems require accurate PoS taggers to produce reliable translations in the target language (TL). The standard statistical approach to PoS ambiguity resolution (or tagging) uses hidden Markov models (HMM) trained in a supervised way from hand-tagged corpora, an expensive resource not always available, or in an unsupervised way through the Baum-Welch expectation-maximization algorithm; both methods use information only from the language being tagged. However, when tagging is considered as an intermediate task for the translation procedure, that is, when the PoS tagger is to be embedded as a module within an MT system, information from the TL can be (unsupervisedly) used in the training phase to increase the translation quality of the whole MT system. This paper presents a method to train HMM-based PoS taggers to be used in MT; the new method uses not only information from the source language (SL), as general-purpose methods do, but also information from the TL and from the remaining modules of the MT system in which the PoS tagger is to be embedded. We find that the translation quality of the MT system embedding a PoS tagger trained in an unsupervised manner through 123 30 F. Sánchez-Martínez et al. this new method is clearly better than that of the same MT system embedding a PoS tagger trained through the Baum-Welch algorithm, and comparable to that obtained by embedding a PoS tagger trained in a supervised way from hand-tagged corpora.

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Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

Khanna

Washington

Tyers

et al. 2021

Machine Translation

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This paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.

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Speeding Up Target-Language Driven Part-of-Speech Tagger Training for Machine Translation

Cited by 3 publications

References 8 publications

Una eina per a una llengua en procés d'estandardització: el traductor automàtic català-sard

Una eina per a una llengua en procés d'estandardització: el traductor automàtic català-sard

Using target-language information to train part-of-speech taggers for machine translation

Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

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