Neural Network-based Word Alignment through Score Aggregation

We present a simple neural network for word alignment that builds source and target word window representations to compute alignment scores for sentence pairs. To enable unsupervised training, we use an aggregation operation that summarizes the alignment scores for a given target word. A soft-margin objective increases scores for true target words while decreasing scores for target words that are not present. Compared to the popular Fast Align model, our approach improves alignment accuracy by 7 AER on English-Czech, by 6 AER on Romanian-English and by 1.7 AER on English-French alignment

Improving Evaluation of English-Czech MT through Paraphrasing

In this paper, we present a method of improving the accuracy of machine translation evaluation of Czech sentences. Given a reference sentence, our algorithm transforms it by targeted paraphrasing into a new synthetic reference sentence that is closer in wording to the machine translation output, but at the same time preserves the meaning of the original reference sentence. Grammatical correctness of~the new reference sentence is provided by applying Depfix on newly created paraphrases. Depfix is a system for post-editing English-to-Czech machine translation outputs. We adjusted it to fix the errors in paraphrased sentences. Due to a noisy source of our paraphrases, we experiment with adding word alignment. However, the alignment reduces the number of paraphrases found and the best results were achieved by~a~simple greedy method with only one-word paraphrases thanks to their intensive filtering. BLEU scores computed using these new reference sentences show significantly higher correlation with human judgment than scores computed on the original reference sentences

Automatické párování tektogramatických stromů z česko-anglického paralelního korpusu

Název práce: Automatické párování tektogramatických stromů z česko-anglického paralelního korpusu Autor: David Mareček Katedra (ústav): Ústav formální a aplikované lingvistiky Vedoucí diplomové práce: Ing. Zdeněk Žabokrtský, Ph.D. Abstrakt: Cílem této práce je implementovat a zhodnotit softwarový nástroj pro automatické zarovnávání (alignment) českých a anglických tektogramatických stromů. Úkolem je najít odpovídajicí si uzly stromů, které reprezentují anglickou větu a její český překlad. Velké množství zarovnaných stromů získaných z paralelního korpusu může být užitečné pro trénování modelu pro transfer strojového překladu. Zároveň může posloužit lingvistům při studování překladových ekvivalentů mezi dvěma jazyky. Výsledky našich experimentů ukazují, že přesunutím problému alignmentu ze slovní roviny na tektogramatickou (a) zvýšíme mezianotátorskou shodu (b) můžeme vytvořit alignovací algoritmus, který využívá i stromovou strukturu věty a překoná nástroj pro alignment GIZA++ spuštěný na uzly tektogramatických stromů. To je pravděpodobně zapříčiněno tím, že tektogramatické reprezentace českých a anglických vět si jsou mnohem podobnější než samotné věty na povrchu. Klíčová slova: tektogramatická rovina, word alignment, strojový překladTitle: Automatic Alignment of Tectogrammatical Trees from Czech-English Parallel Corpus Author: David Mareček Department: Institute of Formal and Applied Linguistics Supervisor: Ing. Zdeněk Žabokrtský, Ph.D. Abstract: The goal of this thesis is to implement and evaluate a software tool for automatic alignment of Czech and English tectogrammatical trees. The task is to find correspondent nodes between two trees that represent an English sentence and its Czech translation. Great amount of aligned trees acquired from parallel corpora can be used for training transfer models for machine translation systems. It is also useful for linguists in studying translation equivalents in two languages. In this thesis there is also described word alignment annotation process. The manual word alignment was necessary for evaluation of the aligner. The results of our experiments show that shifting the alignment task from the word layer to the tectogrammatical layer both (a) increases the interannotator agreement on the task and (b) allows to construct a feature-based algorithm which uses sentence structure and which outperforms the GIZA++ aligner in terms of f-measure on aligned tectogrammatical node pairs. This is probably caused by the fact that tectogrammatical representations of Czech and English sentences are much closer...Ústav formální a aplikované lingvistikyInstitute of Formal and Applied LinguisticsFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

An augmented three-pass system combination framework: DCU combination system for WMT 2010

This paper describes the augmented threepass system combination framework of the Dublin City University (DCU) MT group for the WMT 2010 system combination task. The basic three-pass framework includes building individual confusion networks (CNs), a super network, and a modified Minimum Bayes-risk (mCon- MBR) decoder. The augmented parts for WMT2010 tasks include 1) a rescoring component which is used to re-rank the N-best lists generated from the individual CNs and the super network, 2) a new hypothesis alignment metric – TERp – that is used to carry out English-targeted hypothesis alignment, and 3) more different backbone-based CNs which are employed to increase the diversity of the mConMBR decoding phase. We took part in the combination tasks of Englishto- Czech and French-to-English. Experimental results show that our proposed combination framework achieved 2.17 absolute points (13.36 relative points) and 1.52 absolute points (5.37 relative points) in terms of BLEU score on English-to- Czech and French-to-English tasks respectively than the best single system. We also achieved better performance on human evaluation

MATREX: the DCU MT System for WMT 2008

In this paper, we give a description of the machine translation system developed at DCU that was used for our participation in the evaluation campaign of the Third Workshop on Statistical Machine Translation at ACL 2008. We describe the modular design of our data driven MT system with particular focus on the components used in this participation. We also describe some of the significant modules which were unused in this task. We participated in the EuroParl task for the following translation directions: Spanish–English and French–English, in which we employed our hybrid EBMT-SMT architecture to translate. We also participated in the Czech–English News and News Commentary tasks which represented a previously untested language pair for our system. We report results on the provided development and test sets

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines

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