1,040 research outputs found
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Learning from Noisy Data in Statistical Machine Translation
In dieser Arbeit wurden Methoden entwickelt, die in der Lage sind die negativen
Effekte von verrauschten Daten in SMT Systemen zu senken und dadurch die Leistung des
Systems zu steigern. Hierbei wird das Problem in zwei verschiedenen Schritten des
Lernprozesses behandelt: Bei der Vorverarbeitung und während der
Modellierung. Bei der Vorverarbeitung werden zwei Methoden zur Verbesserung der
statistischen Modelle durch die Erhöhung der Qualität von Trainingsdaten entwickelt.
Bei der Modellierung werden verschiedene Möglichkeiten vorgestellt, um Daten nach ihrer Nützlichkeit zu gewichten.
Zunächst wird der Effekt des Entfernens von False-Positives vom Parallel Corpus
gezeigt. Ein Parallel Corpus besteht aus einem Text in zwei Sprachen,
wobei jeder Satz einer Sprache mit dem entsprechenden Satz der
anderen Sprache gepaart ist. Hierbei wird vorausgesetzt, dass die Anzahl
der Sätzen in beiden Sprachversionen gleich ist. False-Positives in diesem
Sinne sind Satzpaare, die im Parallel Corpus gepaart sind aber keine Übersetzung voneinander sind.
Um diese zu erkennen wird ein kleiner und fehlerfreier
paralleler Corpus (Clean Corpus) vorausgesetzt. Mit Hilfe verschiedenen
lexikalischen Eigenschaften werden zuverlässig False-Positives vor der
Modellierungsphase gefiltert. Eine wichtige lexikalische Eigenschaft hierbei
ist das vom Clean Corpus erzeugte bilinguale Lexikon.
In der Extraktion dieses bilingualen Lexikons werden verschiedene Heuristiken implementiert, die zu einer verbesserten Leistung führen.
Danach betrachten wir das Problem vom Extrahieren der nützlichsten Teile der Trainingsdaten.
Dabei ordnen wir die Daten basierend auf ihren Bezug zur Zieldomaine.
Dies geschieht unter der Annahme der Existenz eines guten repräsentativen Tuning Datensatzes.
Da solche Tuning Daten typischerweise beschränkte Größe haben,
werden Wortähnlichkeiten benutzt um die Abdeckung der Tuning Daten zu erweitern.
Die im vorherigen Schritt verwendeten Wortähnlichkeiten sind entscheidend für
die Qualität des Verfahrens. Aus diesem Grund werden in der Arbeit verschiedene
automatische Methoden zur Ermittlung von solche Wortähnlichkeiten ausgehend von
monoligual und biligual Corpora vorgestellt. Interessanterweise ist dies auch
bei beschränkten Daten möglich, indem auch monolinguale
Daten, die in großen Mengen zur Verfügung stehen, zur Ermittlung der
Wortähnlichkeit herangezogen werden. Bei bilingualen Daten, die häufig nur in beschränkter Größe zur
Verfügung stehen, können auch weitere Sprachpaare herangezogen werden, die mindestens eine Sprache mit dem
vorgegebenen Sprachpaar teilen.
Im Modellierungsschritt behandeln wir das Problem mit verrauschten Daten, indem die
Trainingsdaten anhand der Güte des Corpus gewichtet werden.
Wir benutzen Statistik signifikante Messgrößen, um die weniger verlässlichen
Sequenzen zu finden und ihre Gewichtung zu reduzieren.
Ähnlich zu den vorherigen Ansätzen, werden Wortähnlichkeiten benutzt um das Problem bei begrenzten Daten zu behandeln.
Ein weiteres Problem tritt allerdings auf sobald die absolute Häufigkeiten mit den gewichteten Häufigkeiten ersetzt werden. In dieser Arbeit werden hierfür Techniken zur Glättung der Wahrscheinlichkeiten in dieser Situation entwickelt.
Die Größe der Trainingsdaten werden problematisch sobald man mit Corpora von erheblichem Volumen arbeitet.
Hierbei treten zwei Hauptschwierigkeiten auf: Die Länge der Trainingszeit und der begrenzte Arbeitsspeicher.
Für das Problem der Trainingszeit wird ein Algorithmus entwickelt, der die rechenaufwendigen Berechnungen auf mehrere Prozessoren mit gemeinsamem Speicher ausführt.
Für das Speicherproblem werden speziale Datenstrukturen und Algorithmen für externe Speicher benutzt.
Dies erlaubt ein effizientes Training von extrem großen Modellne in Hardware mit begrenztem Speicher
Description of the Chinese-to-Spanish rule-based machine translation system developed with a hybrid combination of human annotation and statistical techniques
Two of the most popular Machine Translation (MT) paradigms are rule based (RBMT) and corpus based, which include the statistical systems (SMT). When scarce parallel corpus is available, RBMT becomes particularly attractive. This is the case of the Chinese--Spanish language pair.
This article presents the first RBMT system for Chinese to Spanish. We describe a hybrid method for constructing this system taking advantage of available resources such as parallel corpora that are used to extract dictionaries and lexical and structural transfer rules.
The final system is freely available online and open source. Although performance lags behind standard SMT systems for an in-domain test set, the results show that the RBMT’s coverage is competitive and it outperforms the SMT system in an out-of-domain test set. This RBMT system is available to the general public, it can be further enhanced, and it opens up the possibility of creating future hybrid MT systems.Peer ReviewedPostprint (author's final draft
Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration
Cross-language information retrieval (CLIR), where queries and documents are
in different languages, has of late become one of the major topics within the
information retrieval community. This paper proposes a Japanese/English CLIR
system, where we combine a query translation and retrieval modules. We
currently target the retrieval of technical documents, and therefore the
performance of our system is highly dependent on the quality of the translation
of technical terms. However, the technical term translation is still
problematic in that technical terms are often compound words, and thus new
terms are progressively created by combining existing base words. In addition,
Japanese often represents loanwords based on its special phonogram.
Consequently, existing dictionaries find it difficult to achieve sufficient
coverage. To counter the first problem, we produce a Japanese/English
dictionary for base words, and translate compound words on a word-by-word
basis. We also use a probabilistic method to resolve translation ambiguity. For
the second problem, we use a transliteration method, which corresponds words
unlisted in the base word dictionary to their phonetic equivalents in the
target language. We evaluate our system using a test collection for CLIR, and
show that both the compound word translation and transliteration methods
improve the system performance
Revisiting Low Resource Status of Indian Languages in Machine Translation
Indian language machine translation performance is hampered due to the lack
of large scale multi-lingual sentence aligned corpora and robust benchmarks.
Through this paper, we provide and analyse an automated framework to obtain
such a corpus for Indian language neural machine translation (NMT) systems. Our
pipeline consists of a baseline NMT system, a retrieval module, and an
alignment module that is used to work with publicly available websites such as
press releases by the government. The main contribution towards this effort is
to obtain an incremental method that uses the above pipeline to iteratively
improve the size of the corpus as well as improve each of the components of our
system. Through our work, we also evaluate the design choices such as the
choice of pivoting language and the effect of iterative incremental increase in
corpus size. Our work in addition to providing an automated framework also
results in generating a relatively larger corpus as compared to existing
corpora that are available for Indian languages. This corpus helps us obtain
substantially improved results on the publicly available WAT evaluation
benchmark and other standard evaluation benchmarks.Comment: 10 pages, few figures, Preprint under revie
Proceedings
Proceedings of the Workshop
CHAT 2011: Creation, Harmonization and Application of Terminology Resources.
Editors: Tatiana Gornostay and Andrejs Vasiļjevs.
NEALT Proceedings Series, Vol. 12 (2011).
© 2011 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/16956
The FISKMÖ Project : Resources and Tools for Finnish-Swedish Machine Translation and Cross-Linguistic Research
This paper presents FISKMÖ, a project that focuses on the development of resources and tools for cross-linguistic research and machine translation between Finnish and Swedish. The goal of the project is the compilation of a massive parallel corpus out of translated material collected from web sources, public and private organisations and language service providers in Finland with its two official languages. The project also aims at the development of open and freely accessible translation services for those two languages for the general purpose and for domain-specific use. We have released new data sets with over 3 million translation units, a benchmark test set for MT development, pre-trained neural MT models with high coverage and competitive performance and a self-contained MT plugin for a popular CAT tool. The latter enables offline translation without dependencies on external services making it possible to work with highly sensitive data without compromising security concerns.Peer reviewe
Adaptation of machine translation for multilingual information retrieval in the medical domain
Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve eectiveness of cross-lingual IR.
Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound
splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech–English, German–English, and French–English. MT quality is evaluated on data sets created within the Khresmoi project and IR eectiveness is tested on the CLEF eHealth 2013 data sets.
Results. The search query translation results achieved in our experiments are outstanding – our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech–English, from 23.03 to 40.82 for German–English, and from 32.67 to 40.82 for French–English. This is a 55% improvement on average. In terms of the IR performance on this
particular test collection, a significant improvement over the baseline is achieved only for French–English. For Czech–English and German–English, the increased MT quality does not lead to better IR results.
Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance – better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions
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