Dutch compound splitting for bilingual terminology extraction

Compounds pose a problem for applications that rely on precise word alignments such as bilingual terminology extraction. We therefore developed a state-of-the-art hybrid compound splitter for Dutch that makes use of corpus frequency information and linguistic knowledge. Domain-adaptation techniques are used to combine large out-of-domain and dynamically compiled in-domain frequency lists. We perform an extensive intrinsic evaluation on a Gold Standard set of 50,000 Dutch compounds and a set of 5,000 Dutch compounds belonging to the automotive domain. We also propose a novel methodology for word alignment that makes use of the compound splitter. As compounds are not always translated compositionally, we train the word alignment models twice: a first time on the original data set and a second time on the data set in which the compounds are split into their component parts. The obtained word alignment points are then combined

Letter to Sound Rules for Accented Lexicon Compression

This paper presents trainable methods for generating letter to sound rules from a given lexicon for use in pronouncing out-of-vocabulary words and as a method for lexicon compression. As the relationship between a string of letters and a string of phonemes representing its pronunciation for many languages is not trivial, we discuss two alignment procedures, one fully automatic and one hand-seeded which produce reasonable alignments of letters to phones. Top Down Induction Tree models are trained on the aligned entries. We show how combined phoneme/stress prediction is better than separate prediction processes, and still better when including in the model the last phonemes transcribed and part of speech information. For the lexicons we have tested, our models have a word accuracy (including stress) of 78% for OALD, 62% for CMU and 94% for BRULEX. The extremely high scores on the training sets allow substantial size reductions (more than 1/20). WWW site: http://tcts.fpms.ac.be/synthesis/mbrdicoComment: 4 pages 1 figur

Parallel Treebanks in Phrase-Based Statistical Machine Translation

Given much recent discussion and the shift in focus of the field, it is becoming apparent that the incorporation of syntax is the way forward for the current state-of-the-art in machine translation (MT). Parallel treebanks are a relatively recent innovation and appear to be ideal candidates for MT training material. However, until recently there has been no other means to build them than by hand. In this paper, we describe how we make use of new tools to automatically build a large parallel treebank and extract a set of linguistically motivated phrase pairs from it. We show that adding these phrase pairs to the translation model of a baseline phrase-based statistical MT (PBSMT) system leads to significant improvements in translation quality. We describe further experiments on incorporating parallel treebank information into PBSMT, such as word alignments. We investigate the conditions under which the incorporation of parallel treebank data performs optimally. Finally, we discuss the potential of parallel treebanks in other paradigms of MT

Sentence alignment in DPC: maximizing precision, minimizing human effort

A wide spectrum of multilingual applications have aligned parallel corpora as their prerequisite. The aim of the project described in this paper is to build a multilingual corpus where all sentences are aligned at very high precision with a minimal human effort involved. The experiments on a combination of sentence aligners with different underlying algorithms described in this paper showed that by verifying only those links which were not recognized by at least two aligners, an error rate can be reduced by 93.76% as compared to the performance of the best aligner. Such manual involvement concerned only a small portion of all data (6%). This significantly reduces a load of manual work necessary to achieve nearly 100% accuracy of alignment

Exploiting alignment techniques in MATREX: the DCU machine translation system for IWSLT 2008

In this paper, we give a description of the machine translation (MT) system developed at DCU that was used for our third participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2008). In this participation, we focus on various techniques for word and phrase alignment to improve system quality. Specifically, we try out our word packing and syntax-enhanced word alignment techniques for the Chinese–English task and for the English–Chinese task for the first time. For all translation tasks except Arabic–English, we exploit linguistically motivated bilingual phrase pairs extracted from parallel treebanks. We smooth our translation tables with out-of-domain word translations for the Arabic–English and Chinese–English tasks in order to solve the problem of the high number of out of vocabulary items. We also carried out experiments combining both in-domain and out-of-domain data to improve system performance and, finally, we deploy a majority voting procedure combining a language model based method and a translation-based method for case and punctuation restoration. We participated in all the translation tasks and translated both the single-best ASR hypotheses and the correct recognition results. The translation results confirm that our new word and phrase alignment techniques are often helpful in improving translation quality, and the data combination method we proposed can significantly improve system performance

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure

Pattern-based phylogenetic distance estimation and tree reconstruction

We have developed an alignment-free method that calculates phylogenetic distances using a maximum likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package decaf+py at http://www.bioinformatics.org.au), we have created a dataset of reference trees covering a wide range of phylogenetic distances. Amino acid sequences were evolved along the trees and input to the tested methods; from their calculated distances we infered trees whose topologies we compared to the reference trees. We find our pattern-based method statistically superior to all other tested alignment-free methods on this dataset. We also demonstrate the general advantage of alignment-free methods over an approach based on automated alignments when sequences violate the assumption of collinearity. Similarly, we compare methods on empirical data from an existing alignment benchmark set that we used to derive reference distances and trees. Our pattern-based approach yields distances that show a linear relationship to reference distances over a substantially longer range than other alignment-free methods. The pattern-based approach outperforms alignment-free methods and its phylogenetic accuracy is statistically indistinguishable from alignment-based distances.Comment: 21 pages, 3 figures, 2 table

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