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

A Scalable Architecture for Bilingual Lexicography

SABLE is a Scalable Architecture for Bilingual LExicography. It is designed to produce clean broad-coverage translation lexicons from raw, unaligned parallel texts. Its black-box functionality makes it suitable for naive users. The architecture has been implemented for different language pairs, and has been tested on very large and noisy input. SABLE does not rely on language-specific resources such as part-of-speech taggers, but it can take advantage of them when they are available

Automatic Construction of Chinese-English Translation Lexicons

The process of constructing translation lexicons from parallel texts (bitexts) can be broken down into three stages: mapping bitext correspondence, counting co-occurrences, and estimating a translation model. State-of-the-art techniques for accomplishing each stage of the process had already been developed, but only for bitexts involving fairly similar languages. Correct and efficient implementation of each stage poses special challenges when the parallel texts involve two very different languages. This report describes our theoretical and empirical investigations into how existing techniques might be extended and applied to Chinese/English bitexts

Porting SIMRJGSA Algorithms For Mapping And Alignment To Malay-English Bitexts.

Parallel texts or Bitexts - where the same content is available in several languages, due to document translation, are becoming plentiful and available, both in private data warehouses and on publicly accessible sites on the WWW

Parallel Strands: A Preliminary Investigation into Mining the Web for Bilingual Text

Parallel corpora are a valuable resource for machine translation, but at present their availability and utility is limited by genre- and domain-specificity, licensing restrictions, and the basic difficulty of locating parallel texts in all but the most dominant of the world's languages. A parallel corpus resource not yet explored is the World Wide Web, which hosts an abundance of pages in parallel translation, offering a potential solution to some of these problems and unique opportunities of its own. This paper presents the necessary first step in that exploration: a method for automatically finding parallel translated documents on the Web. The technique is conceptually simple, fully language independent, and scalable, and preliminary evaluation results indicate that the method may be accurate enough to apply without human intervention.Comment: LaTeX2e, 11 pages, 7 eps figures; uses psfig, llncs.cls, theapa.sty. An Appendix at http://umiacs.umd.edu/~resnik/amta98/amta98_appendix.html contains test dat

Translation memory and computer assisted translation tool for medieval texts

Translation memories (TMs), as part of Computer Assisted Translation (CAT) tools, support translators reusing portions of formerly translated text. Fencing books are good candidates for using TMs due to the high number of repeated terms. Medieval texts suffer a number of drawbacks that make hard even “simple” rewording to the modern version of the same language. The analyzed difficulties are: lack of systematic spelling, unusual word orders and typos in the original. A hypothesis is made and verified that even simple modernization increases legibility and it is feasible, also it is worthwhile to apply translation memories due to the numerous and even extremely long repeated terms. Therefore, methods and algorithms are presented 1. for automated transcription of medieval texts (when a limited training set is available), and 2. collection of repeated patterns. The efficiency of the algorithms is analyzed for recall and precision

Dutch parallel corpus: a balanced parallel corpus for Dutch-English and Dutch-French

status: publishe

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language

Sentence Alignment using MR and GA

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on mathematical regression (MR) and genetic algorithm (GA) classifiers are presented. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the mathematical regression and genetic algorithm models. Another set of data was used for testing. The results of (MR) and (GA) outperform the results of length based approach. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research

Example-based machine translation using the marker hypothesis

The development of large-scale rules and grammars for a Rule-Based Machine Translation (RBMT) system is labour-intensive, error-prone and expensive. Current research in Machine Translation (MT) tends to focus on the development of corpus-based systems which can overcome the problem of knowledge acquisition. Corpus-Based Machine Translation (CBMT) can take the form of Statistical Machine Translation (SMT) or Example-Based Machine Translation (EBMT). Despite the benefits of EBMT, SMT is currently the dominant paradigm and many systems classified as example-based integrate additional rule-based and statistical techniques. The benefits of an EBMT system which does not require extensive linguistic resources and can produce reasonably intelligible and accurate translations cannot be overlooked. We show that our linguistics-lite EBMT system can outperform an SMT system trained on the same data. The work reported in this thesis describes the development of a linguistics-lite EBMT system which does not have recourse to extensive linguistic resources. We apply the Marker Hypothesis (Green, 1979) — a psycholinguistic theory which states that all natural languages are ‘marked’ for complex syntactic structure at surface form by a closed set of specific lexemes and morphemes. We use this technique in different environments to segment aligned (English, French) phrases and sentences. We then apply an alignment algorithm which can deduce smaller aligned chunks and words. Following a process similar to (Block, 2000), we generalise these alignments by replacing certain function words with an associated tag. In so doing, we cluster on marker words and add flexibility to our matching process. In a post hoc stage we treat the World Wide Web as a large corpus and validate and correct instances of determiner-noun and noun-verb boundary friction. We have applied our marker-based EBMT system to different bitexts and have explored its applicability in various environments. We have developed a phrase-based EBMT system (Gough et al., 2002; Way and Gough, 2003). We show that despite the perceived low quality of on-line MT systems, our EBMT system can produce good quality translations when such systems are used to seed its memories. (Carl, 2003a; Schaler et al., 2003) suggest that EBMT is more suited to controlled translation than RBMT as it has been known to overcome the ‘knowledge acquisition bottleneck’. To this end, we developed the first controlled EBMT system (Gough and Way, 2003; Way and Gough, 2004). Given the lack of controlled bitexts, we used an on-line MT system Logomedia to translate a set of controlled English sentences, We performed experiments using controlled analysis and generation and assessed the performance of our system at each stage. We made a number of improvements to our sub-sentential alignment algorithm and following some minimal adjustments to our system, we show that our controlled EBMT system can outperform an RBMT system. We applied the Marker Hypothesis to a more scalable data set. We trained our system on 203,529 sentences extracted from a Sun Microsystems Translation Memory. We thus reduced problems of data-sparseness and limited our dependence on Logomedia. We show that scaling up data in a marker-based EBMT system improves the quality of our translations. We also report on the benefits of extracting lexical equivalences from the corpus using Mutual Information