Towards Parallel Czech-Russian Dependency Treebank

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 44-52. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

A Report on the Third VarDial Evaluation Campaign

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Lingvistické otázky ve strojovém překladu mezi češtinou a ruštinou

V této disertační práci zkoumáme strojový překlad mezi češtinou a ruštinou z hlediska lingvisty. Pracujeme s několika pravidlovými a statistickými překladovými systémy a pomocí změn v jejích nastavení se snážíme dosáhnout co nejlepších výsledků překladu. Jedna z otázek, které řešíme v naší práci, je nakolik příbuznost obou jazyků pomáhá strojovému překladu. Hlavním cílem práce je lingvistický rozbor chyb ve výstupu čtyř systémů strojového překladu, dvou experimentálních - TectoMT, Moses, a dvou komerčních - PC Translator a Google Translate. Analyzujeme každý typ chyb a řešíme, zda daná chyba souvisí s rozdílem mezi češtinou a ruštinou nebo zda je zapříčiněná architecturou jednotlivých systémů. Pro některé chyby nabízíme cesty, jak je opravit. Ve zvláštní kapitole se zaměřujeme na chyby v povrchové valenci sloves. Zkoumáme rozdíly v české a ruské povrchové valenci, popisujeme extrakci slovníku povrchových forem a tento slovník integrujeme do systému TectoMT. Dále nabízíme souhrn lingvistických pozorování o povaze rozdílů v české a ruské valenci. Powered by TCPDF (www.tcpdf.org)In this thesis we analyze machine translation between Czech and Russian languages from the perspective of a linguist. We work with two types of Machine Translation systems - rule-based (TectoMT) and statistical (Moses). We experiment with different setups of these two systems in order to achieve the best possible quality. One of the questions we address in our work is whether relatedness of the discussed languages has some impact on machine translation. We explore the output of our two experimental systems and two commercial systems: PC Translator and Google Translate. We make a linguistically-motivated classification of errors for the language pair and describe each type of error in detail, analyzing whether it occurred due to some difference between Czech and Russian or is it caused by the system architecture. We then compare the usage of some specific linguistic phenomena in the two languages and state how the individual systems cope with mismatches. For some errors, we suggest ways to improve them and in several cases we implement those suggestions. In particular, we focus on one specific error type - surface valency. We research the mismatches between Czech and Russian valency, extract a lexicon of surface valency frames, incorporate the lexicon into the TectoMT translation pipeline and present...Institute of Formal and Applied LinguisticsÚstav formální a aplikované lingvistikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Linguistic Issues in Machine Translation between Czech and Russian

In this thesis we analyze machine translation between Czech and Russian languages from the perspective of a linguist. We work with two types of Machine Translation systems - rule-based (TectoMT) and statistical (Moses). We experiment with different setups of these two systems in order to achieve the best possible quality. One of the questions we address in our work is whether relatedness of the discussed languages has some impact on machine translation. We explore the output of our two experimental systems and two commercial systems: PC Translator and Google Translate. We make a linguistically-motivated classification of errors for the language pair and describe each type of error in detail, analyzing whether it occurred due to some difference between Czech and Russian or is it caused by the system architecture. We then compare the usage of some specific linguistic phenomena in the two languages and state how the individual systems cope with mismatches. For some errors, we suggest ways to improve them and in several cases we implement those suggestions. In particular, we focus on one specific error type - surface valency. We research the mismatches between Czech and Russian valency, extract a lexicon of surface valency frames, incorporate the lexicon into the TectoMT translation pipeline and present..

UMC 0.1: Czech-Russian-English Multilingual Corpus

UMC 0.1 Czech-English-Russian is a multilingual parallel corpus of texts in Czech, Russian and English languages with automatic pairwise sentence alignments. The primary aim of UMC is to extend the set of languages covered by the corpus CzEng mainly for the purposes of machine translation. All the texts were downloaded from a single source — The Project Syndicate (Copyright: Project Syndicate 1995-2008), which contains a huge collection of high-quality news articles and commentaries. We were given the permission to use the texts for research and non-commercial purposes

UMC 0.1: Czech-Russian-English Multilingual Corpus

UMC 0.1 Czech-English-Russian is a multilingual parallel corpus of texts in Czech, Russian and English languages with automatic pairwise sentence alignments. The primary aim of UMC is to extend the set of languages covered by the corpus CzEng mainly for the purposes of machine translation. All the texts were downloaded from a single source — The Project Syndicate (Copyright: Project Syndicate 1995-2008), which contains a huge collection of high-quality news articles and commentaries. We were given the permission to use the texts for research and non-commercial purposes

Incorporation of a valency lexicon into a TectoMT pipeline

In this paper, we focus on the incorporation of a valency lexicon into TectoMT system for Czech-Russian language pair. We demonstrate valency errors in MT output and describe how the introduction of a lexicon influenced the translation results. Though there was no impact on BLEU score, the manual inspection of concrete cases showed some improvement

Improving a Neural-based Tagger for Multiword Expression Identification

In this paper, we present a set of improvements introduced to MUMULS, a tagger for the automatic detection of verbal multiword expressions. Our tagger participated in the PARSEME shared task and it was the only one based on neural networks. We show that character-level embeddings can improve the performance, mainly by reducing the out-of-vocabulary rate. Furthermore, replacing the softmax layer in the decoder by a conditional random field classifier brings additional improvements. Finally, we compare different context-aware feature representations of input tokens using various encoder architectures. The experiments on Czech show that the combination of character-level embeddings using a convolutional network, self-attentive encoding layer over the word representations and an output conditional random field classifier yields the best empirical results