181 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
Using BabelNet to improve OOV coverage in SMT
Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies
different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic
dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named
entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods –
using direct training data, domain-adaptation techniques and the BabelNet API – are proposed in this paper to obtain translations for
OOVs to improve system performance. Experimental results on English–Polish and English–Chinese language pairs show that domain
adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is
a really useful tool for improving translation performance of SMT systems
Mixed-Language Arabic- English Information Retrieval
Includes abstract.Includes bibliographical references.This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries
Improved cross-language information retrieval via disambiguation and vocabulary discovery
Cross-lingual information retrieval (CLIR) allows people to find documents irrespective of the language used in the query or document. This thesis is concerned with the development of techniques to improve the effectiveness of Chinese-English CLIR. In Chinese-English CLIR, the accuracy of dictionary-based query translation is limited by two major factors: translation ambiguity and the presence of out-of-vocabulary (OOV) terms. We explore alternative methods for translation disambiguation, and demonstrate new techniques based on a Markov model and the use of web documents as a corpus to provide context for disambiguation. This simple disambiguation technique has proved to be extremely robust and successful. Queries that seek topical information typically contain OOV terms that may not be found in a translation dictionary, leading to inappropriate translations and consequent poor retrieval performance. Our novel OOV term translation method is based on the Chinese authorial practice of including unfamiliar English terms in both languages. It automatically extracts correct translations from the web and can be applied to both Chinese-English and English-Chinese CLIR. Our OOV translation technique does not rely on prior segmentation and is thus free from seg mentation error. It leads to a significant improvement in CLIR effectiveness and can also be used to improve Chinese segmentation accuracy. Good quality translation resources, especially bilingual dictionaries, are valuable resources for effective CLIR. We developed a system to facilitate construction of a large-scale translation lexicon of Chinese-English OOV terms using the web. Experimental results show that this method is reliable and of practical use in query translation. In addition, parallel corpora provide a rich source of translation information. We have also developed a system that uses multiple features to identify parallel texts via a k-nearest-neighbour classifier, to automatically collect high quality parallel Chinese-English corpora from the web. These two automatic web mining systems are highly reliable and easy to deploy. In this research, we provided new ways to acquire linguistic resources using multilingual content on the web. These linguistic resources not only improve the efficiency and effectiveness of Chinese-English cross-language web retrieval; but also have wider applications than CLIR
CLIR teknikak baliabide urriko hizkuntzetarako
152 p.Hizkuntza arteko informazioaren berreskurapenerako sistema bat garatxean kontsulta itzultzea da hizkuntzaren mugari aurre egiteko hurbilpenik erabiliena. Kontsulta itzultzeko estrategia arrakastatsuenak itzulpen automatikoko sistem aedo corpus paraleloetan oinarritzen dira, baina baliabide hauek urriak dira baliabide urriko hizkuntzen eszenatokietan. Horrelako egoeretan egokiagoa litzateke eskuragarriago diren baliabideetan oinarritutako komtsulta itzultzeko estrategia bat. Tesi honetan frogatu nahi dugu baliabide nagusi horiek hiztegi elebiduna eta horren osagarri diren corpus konparagarriak eta kontsulta-saioak izan daitezkeela. // Hizkuntza arteko informazioaren berreskurapenerako sistema bat garatxean kontsulta itzultzea da hizkuntzaren mugari aurre egiteko hurbilpenik erabiliena. Kontsulta itzultzeko estrategia arrakastatsuenak itzulpen automatikoko sistem aedo corpus paraleloetan oinarritzen dira, baina baliabide hauek urriak dira baliabide urriko hizkuntzen eszenatokietan. Horrelako egoeretan egokiagoa litzateke eskuragarriago diren baliabideetan oinarritutako komtsulta itzultzeko estrategia bat. Tesi honetan frogatu nahi dugu baliabide nagusi horiek hiztegi elebiduna eta horren osagarri diren corpus konparagarriak eta kontsulta-saioak izan daitezkeela
Augmenting Translation Lexica by Learning Generalised Translation Patterns
Bilingual Lexicons do improve quality: of parallel corpora alignment, of newly extracted
translation pairs, of Machine Translation, of cross language information retrieval, among
other applications. In this regard, the first problem addressed in this thesis pertains to
the classification of automatically extracted translations from parallel corpora-collections
of sentence pairs that are translations of each other. The second problem is concerned
with machine learning of bilingual morphology with applications in the solution of first
problem and in the generation of Out-Of-Vocabulary translations.
With respect to the problem of translation classification, two separate classifiers for
handling multi-word and word-to-word translations are trained, using previously extracted
and manually classified translation pairs as correct or incorrect. Several insights
are useful for distinguishing the adequate multi-word candidates from those that are
inadequate such as, lack or presence of parallelism, spurious terms at translation ends
such as determiners, co-ordinated conjunctions, properties such as orthographic similarity
between translations, the occurrence and co-occurrence frequency of the translation
pairs. Morphological coverage reflecting stem and suffix agreements are explored as key
features in classifying word-to-word translations. Given that the evaluation of extracted
translation equivalents depends heavily on the human evaluator, incorporation of an
automated filter for appropriate and inappropriate translation pairs prior to human evaluation
contributes to tremendously reduce this work, thereby saving the time involved
and progressively improving alignment and extraction quality. It can also be applied
to filtering of translation tables used for training machine translation engines, and to
detect bad translation choices made by translation engines, thus enabling significative
productivity enhancements in the post-edition process of machine made translations.
An important attribute of the translation lexicon is the coverage it provides. Learning
suffixes and suffixation operations from the lexicon or corpus of a language is an extensively
researched task to tackle out-of-vocabulary terms. However, beyond mere words
or word forms are the translations and their variants, a powerful source of information
for automatic structural analysis, which is explored from the perspective of improving
word-to-word translation coverage and constitutes the second part of this thesis. In this
context, as a phase prior to the suggestion of out-of-vocabulary bilingual lexicon entries,
an approach to automatically induce segmentation and learn bilingual morph-like units by identifying and pairing word stems and suffixes is proposed, using the bilingual
corpus of translations automatically extracted from aligned parallel corpora, manually
validated or automatically classified. Minimally supervised technique is proposed to enable
bilingual morphology learning for language pairs whose bilingual lexicons are highly
defective in what concerns word-to-word translations representing inflection diversity.
Apart from the above mentioned applications in the classification of machine extracted
translations and in the generation of Out-Of-Vocabulary translations, learned bilingual
morph-units may also have a great impact on the establishment of correspondences of
sub-word constituents in the cases of word-to-multi-word and multi-word-to-multi-word
translations and in compression, full text indexing and retrieval applications
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