1,399 research outputs found
Example-based machine translation of the Basque language
Basque is both a minority and a highly inflected language with free order of sentence constituents. Machine Translation of Basque is thus both a real need and a test bed for MT techniques. In this paper, we present a modular Data-Driven MT system which includes different chunkers as well as chunk aligners which can deal with the free order of sentence constituents of Basque. We conducted Basque to English translation experiments, evaluated on a large corpus
(270, 000 sentence pairs). The experimental results show that our system significantly outperforms state-of-the-art
approaches according to several common automatic evaluation metrics
Comparing rule-based and data-driven approaches to Spanish-to-Basque machine translation
In this paper, we compare the rule-based and data-driven
approaches in the context of Spanish-to-Basque Machine Translation. The rule-based system we consider has been developed specifically for Spanish-to-Basque machine translation, and is tuned to this language pair. On the contrary, the data-driven system we use is generic, and has not been specifically designed to deal with Basque. Spanish-to-Basque Machine Translation is a challenge for data-driven
approaches for at least two reasons. First, there is lack of
bilingual data on which a data-driven MT system can be trained. Second, Basque is a morphologically-rich agglutinative language and translating to Basque requires a huge generation of morphological information, a difficult task for a generic system not specifically tuned to Basque. We present the results of a series of experiments, obtained on two different corpora, one being “in-domain” and the
other one “out-of-domain” with respect to the data-driven
system. We show that n-gram based automatic evaluation and edit-distance-based human evaluation yield two different sets of results. According to BLEU, the data-driven system outperforms the rule-based system on the in-domain data, while according to the human evaluation, the rule-based
approach achieves higher scores for both corpora
MATREX: DCU machine translation system for IWSLT 2006
In this paper, we give a description of the machine translation system developed at DCU that was used for our first participation in the evaluation campaign of the International Workshop on Spoken Language Translation (2006). This system combines two types of approaches. First, we use an EBMT approach to collect aligned chunks based on two steps: deterministic chunking of both sides and chunk alignment. We use several chunking and alignment strategies. We also extract SMT-style aligned phrases, and the two types of resources are combined.
We participated in the Open Data Track for the following
translation directions: Arabic-English and Italian-English,
for which we translated both the single-best ASR hypotheses
and the text input. We report the results of the system for
the provided evaluation sets
Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
Knowledge-based machine translation (KBMT) techniques yield high quality in
domains with detailed semantic models, limited vocabulary, and controlled input
grammar. Scaling up along these dimensions means acquiring large knowledge
resources. It also means behaving reasonably when definitive knowledge is not
yet available. This paper describes how we can fill various KBMT knowledge
gaps, often using robust statistical techniques. We describe quantitative and
qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT
system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9
Reordering in statistical machine translation
PhDMachine translation is a challenging task that its difficulties arise from several characteristics
of natural language. The main focus of this work is on reordering as one of
the major problems in MT and statistical MT, which is the method investigated in this
research. The reordering problem in SMT originates from the fact that not all the words
in a sentence can be consecutively translated. This means words must be skipped and
be translated out of their order in the source sentence to produce a fluent and grammatically
correct sentence in the target language. The main reason that reordering is
needed is the fundamental word order differences between languages. Therefore, reordering
becomes a more dominant issue, the more source and target languages are
structurally different.
The aim of this thesis is to study the reordering phenomenon by proposing new methods
of dealing with reordering in SMT decoders and evaluating the effectiveness of
the methods and the importance of reordering in the context of natural language processing
tasks. In other words, we propose novel ways of performing the decoding to
improve the reordering capabilities of the SMT decoder and in addition we explore
the effect of improving the reordering on the quality of specific NLP tasks, namely
named entity recognition and cross-lingual text association. Meanwhile, we go beyond
reordering in text association and present a method to perform cross-lingual text fragment
alignment, based on models of divergence from randomness.
The main contribution of this thesis is a novel method named dynamic distortion,
which is designed to improve the ability of the phrase-based decoder in performing
reordering by adjusting the distortion parameter based on the translation context. The
model employs a discriminative reordering model, which is combining several fea-
2
tures including lexical and syntactic, to predict the necessary distortion limit for each
sentence and each hypothesis expansion. The discriminative reordering model is also
integrated into the decoder as an extra feature. The method achieves substantial improvements
over the baseline without increase in the decoding time by avoiding reordering
in unnecessary positions.
Another novel method is also presented to extend the phrase-based decoder to dynamically
chunk, reorder, and apply phrase translations in tandem. Words inside the chunks
are moved together to enable the decoder to make long-distance reorderings to capture
the word order differences between languages with different sentence structures.
Another aspect of this work is the task-based evaluation of the reordering methods and
other translation algorithms used in the phrase-based SMT systems. With more successful
SMT systems, performing multi-lingual and cross-lingual tasks through translating
becomes more feasible. We have devised a method to evaluate the performance
of state-of-the art named entity recognisers on the text translated by a SMT decoder.
Specifically, we investigated the effect of word reordering and incorporating reordering
models in improving the quality of named entity extraction.
In addition to empirically investigating the effect of translation in the context of crosslingual
document association, we have described a text fragment alignment algorithm
to find sections of the two documents in different languages, that are content-wise related.
The algorithm uses similarity measures based on divergence from randomness
and word-based translation models to perform text fragment alignment on a collection
of documents in two different languages.
All the methods proposed in this thesis are extensively empirically examined. We have
tested all the algorithms on common translation collections used in different evaluation
campaigns. Well known automatic evaluation metrics are used to compare the
suggested methods to a state-of-the art baseline and results are analysed and discussed
A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora
Statistical and rule-based methods are complementary approaches to machine translation (MT) that have different strengths and weaknesses. This complementarity has, over the last few years, resulted in the consolidation of a growing interest in hybrid systems that combine both data-driven and linguistic approaches. In this paper, we address the situation in which the amount of bilingual resources that is available for a particular language pair is not sufficiently large to train a competitive statistical MT system, but the cost and slow development cycles of rule-based MT systems cannot be afforded either. In this context, we formalise a new method that uses scarce parallel corpora to automatically infer a set of shallow-transfer rules to be integrated into a rule-based MT system, thus avoiding the need for human experts to handcraft these rules. Our work is based on the alignment template approach to phrase-based statistical MT, but the definition of the alignment template is extended to encompass different generalisation levels. It is also greatly inspired by the work of Sánchez-Martínez and Forcada (2009) in which alignment templates were also considered for shallow-transfer rule inference. However, our approach overcomes many relevant limitations of that work, principally those related to the inability to find the correct generalisation level for the alignment templates, and to select the subset of alignment templates that ensures an adequate segmentation of the input sentences by the rules eventually obtained. Unlike previous approaches in literature, our formalism does not require linguistic knowledge about the languages involved in the translation. Moreover, it is the first time that conflicts between rules are resolved by choosing the most appropriate ones according to a global minimisation function rather than proceeding in a pairwise greedy fashion. Experiments conducted using five different language pairs with the free/open-source rule-based MT platform Apertium show that translation quality significantly improves when compared to the method proposed by Sánchez-Martínez and Forcada (2009), and is close to that obtained using handcrafted rules. For some language pairs, our approach is even able to outperform them. Moreover, the resulting number of rules is considerably smaller, which eases human revision and maintenance.Research funded by Universitat d’Alacant through project GRE11-20, by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF/2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran)
Machine translation evaluation resources and methods: a survey
We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT.
This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content
An example-based approach to translating sign language
Users of sign languages are often forced to use a language in which they have reduced competence simply because documentation in their preferred format is not available. While some research exists on translating between natural and sign languages, we present here what we believe to be the first attempt to tackle this problem using an example-based (EBMT) approach.
Having obtained a set of English–Dutch Sign Language examples, we employ an approach to EBMT using the ‘Marker Hypothesis’ (Green, 1979), analogous to the successful system of (Way & Gough, 2003), (Gough & Way, 2004a) and (Gough & Way, 2004b). In a set of experiments, we show that
encouragingly good translation quality may be obtained using such an approach
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