ufal/bilingual-abstracts-corpus huggingface dataset:
Manually Classified Errors in English-Slovak Translation
Ručne klasifikované chyby v anglicko-slovenskom preklade
Year: 2012
Authors: Petra Galuščáková, Ondřej Bojar

Abstract (English):
Outputs of three English-Slovak machine translation systems for 50 sentences randomly selected from WMT 2011 test set. The translations were manually processed and the errors were marked and classified according to the scheme by Vilar et al. (David Vilar, Jia Xu, Luis Fernando D’Haro, Hermann Ney: Error Analysis of Statistical Machine Translation Output, Proceedings of LREC-2006, 2006)

Abstract (Czech):
Automatický preklad z angličtiny do slovenčiny 50 viet náhodne vybraných z testovacej sady WMT 2011 pomocou troch prekladových systémov. V prekladoch boli ručne označené chyby, ktoré boli ďalej klasifikované podľa schémy danej v článku (David Vilar, Jia Xu, Luis Fernando D’Haro, Hermann Ney: Error Analysis of Statistical Machine Translation Output, Proceedings of LREC-2006, 2006).
WMT 2011 Testing Set in Slovak
Testovacia sada WMT 2011 preložená do slovenčiny
Year: 2012
Authors: Petra Galuščáková, Ondřej Bojar

Abstract (English):
WMT 2011 Workshop testing set manually translated from Czech and English into Slovak.

Abstract (Czech):
Testovacia sada z workshopu WMT 2011 manuálne preložená z češtiny a angličtiny do slovenčiny.
CUNI at MediaEval 2012 Search and Hyperlinking Task
CUNI na MediaEvale 2012, Search and Hyperlinking Task
Year: 2012
Authors: Petra Galuščáková, Pavel Pecina

Abstract (English):
The paper describes the Charles University setup used in the Search and Hyperlinking task of the MediaEval 2012 Multimedia Benchmark. We applied the Terrier retrieval system to the automatic transcriptions of the video recordings segmented into shorter parts and searched for those relevant to given queries. Two strategies were applied for segmentation of the recordings: one based on regular segmentation according to time and the second based on semantic segmentation by the TextTiling algorithm. The best results were achieved by the Hiemstra and TF-IDF models on the LIMSI transcripts and various segmentation.

Abstract (Czech):
Článok popisuje postup, ktorý sme použili v úlohe Search and Hyperlinking  v MediaEval 2012 Multimedia Benchmark. Pri riešení bol použitý systém Terrier, ktorý bol aplikovaný na automatické prepisy video nahrávok. Nahrávky boli segmentované na menšie časti, ktoré boli ďalej prehľadané. Pri segmentácii boli použité dva prístupy: jeden založený na pravidelnej segmentácii podľa času a druhý založený na sémantickej segmentácii pomocou algoritmu TextTiling. Najlepší výsledok bol dosiahnutý pomocou pravidelnej segmentácie, pomocou modelov Hiemstra a TF-IDF na prepisoch LIMSI.
Penalty Functions for Evaluation Measures of Unsegmented Speech Retrieval
Penalizačné funkcie v metrikách na vyhodnocovanie vyhľadávania v hovorenej reči
Year: 2012
Authors: Petra Galuščáková, Pavel Pecina, Jan Hajič

Abstract (English):
This paper deals with evaluation of information retrieval from unsegmented speech. We focus on Mean Generalized Average Precision, the evaluation measure widely used for unsegmented speech retrieval. This measure is designed to allow certain tolerance in matching
retrieval results (starting points of relevant segments) against a gold standard relevance assessment. It employs a Penalty Function which evaluates non-exact matches in the retrieval results based on their distance from the beginnings of their nearest true relevant segments. However, the choice of the Penalty Function is usually ad-hoc and does not necessary reﬂect users’ perception of the speech retrieval quality. We perform a lab test to study satisfaction of users of a speech retrieval system to empirically estimate the optimal shape of the Penalty Function.

Abstract (Czech):
Článok sa zaoberá vyhodnocovaním vyhľadávania v nesegmentovanej hovorenej reči. Zameriavame sa na metriku Mean Generalized Average precision, ktorá sa vyhodnocovanie vyhľadávania v reči často využíva. Táto metrika je navrhnutá tak, aby tolerovala určitú odchýlku medzi automaticky vyhľadanými výsledkami (začiatočnými bodmi relevantných segmentov) a  ručne označenými výsledkami. Na tento účel používa metrika penalizačnú funkciu, ktorá určuje kvalitu vyhľadaných úsekov na základe ich vzdialenosti ku najbližšiemu začiatku ručne označeného relevantného úseku. Výber penalizačnej funkcie však nemusí zodpovedať kvalite vyhľadávania z pohľadu užívateľa. V článku popisujeme užívateľskú štúdiu, ktorou sme skúmali spokojnosť užívateľa pri vyhľadávaní informácií. Na základe výsledkov sme navrhli optimálnu penalizačnú funkciu.
A Dataset Comparison for an Indonesian-English Statistical Machine Translation System
N/A
Year: 2012
Authors: Septina Dian Larasati

Abstract (English):
In this paper, we study the effect of incorporating morphological information on an Indonesian (id) to English (en) Statistical Machine Translation (SMT) system as part of a preprocessing module. The linguistic phenomenon that is being addressed here is Indonesian cliticized words. The approach is to transform the text by separating the correct clitics from a cliticized word to simplify the word alignment. We also study the effect of applying the preprocessing on different SMT systems trained on different kinds of text, such as spoken language text. The system is built using the state-of-the-art SMT tool, MOSES. The Indonesian morphological information is provided by MorphInd. Overall the preprocessing improves the translation quality, especially for the Indonesian spoken language text, where it gains 1.78 BLEU score points of increase.

Abstract (Czech):
(Not yet available. English version repeated)
In this paper, we study the effect of incorporating morphological information on an Indonesian (id) to English (en) Statistical Machine Translation (SMT) system as part of a preprocessing module. The linguistic phenomenon that is being addressed here is Indonesian cliticized words. The approach is to transform the text by separating the correct clitics from a cliticized word to simplify the word alignment. We also study the effect of applying the preprocessing on different SMT systems trained on different kinds of text, such as spoken language text. The system is built using the state-of-the-art SMT tool, MOSES. The Indonesian morphological information is provided by MorphInd. Overall the preprocessing improves the translation quality, especially for the Indonesian spoken language text, where it gains 1.78 BLEU score points of increase.
IDENTIC Corpus: Morphologically Enriched Indonesian-English Parallel Corpus
IDENTIC Corpus: Indonésko-anglický paralelní korpus obohacený o morfologii
Year: 2012
Authors: Septina Dian Larasati

Abstract (English):
This paper describes the creation process of an Indonesian-English parallel corpus (IDENTIC). The corpus contains 45,000 sentences collected from different sources in different genres. Several manual text preprocessing tasks, such as alignment and spelling correction, are applied to the corpus to assure its quality. We also apply language specific text processing such as tokenization on both sides and clitic normalization on the Indonesian side. The corpus is available in two different formats: ‘plain’, stored in text format and ‘morphologically enriched’, stored in CoNLL format. Some parts of the corpus are publicly available at the IDENTIC homepage.

Abstract (Czech):
This paper describes the creation process of an Indonesian-English parallel corpus (IDENTIC). The corpus contains 45,000 sentences collected from different sources in different genres. Several manual text preprocessing tasks, such as alignment and spelling correction, are applied to the corpus to assure its quality. We also apply language specific text processing such as tokenization on both sides and clitic normalization on the Indonesian side. The corpus is available in two different formats: ‘plain’, stored in text format and ‘morphologically enriched’, stored in CoNLL format. Some parts of the corpus are publicly available at the IDENTIC homepage
Improving Word Alignment by Exploiting Adapted Word Similarity
N/A
Year: 2012
Authors: Septina Dian Larasati

Abstract (English):
This paper presents a method to improve a word alignment model in a phrase-based Statistical Machine Translation system for a low resourced language using a string similarity approach. Our method captures similar words that can be seen as semi-monolingual across languages, such as numbers, named entities, and adapted/loan words. We use several string similarity metrics to measure the monolinguality of the words, such as Longest Common Subsequence Ratio (LCSR), Minimum Edit Distance Ratio (MEDR), and we also use a modified BLEU Score (modBLEU).
Our approach is to add intersecting alignment points for word pairs that are orthographically similar, before applying a word alignment heuristic, to generate a better word alignment.
We demonstrate this approach on Indonesian-to-English translation task, where the languages share many similar words that are poorly aligned given a limited training data.
This approach gives a statistically significant improvement by up to 0.66 in terms of BLEU score.

Abstract (Czech):
(Not yet available. English version repeated)
This paper presents a method to improve a word alignment model in a phrase-based Statistical Machine Translation system for a low resourced language using a string similarity approach. Our method captures similar words that can be seen as semi-monolingual across languages, such as numbers, named entities, and adapted/loan words. We use several string similarity metrics to measure the monolinguality of the words, such as Longest Common Subsequence Ratio (LCSR), Minimum Edit Distance Ratio (MEDR), and we also use a modified BLEU Score (modBLEU).
Our approach is to add intersecting alignment points for word pairs that are orthographically similar, before applying a word alignment heuristic, to generate a better word alignment.
We demonstrate this approach on Indonesian-to-English translation task, where the languages share many similar words that are poorly aligned given a limited training data.
This approach gives a statistically significant improvement by up to 0.66 in terms of BLEU score.
Towards an Indonesian-English SMT System: A Case Study of an Under-Studied and Under-Resourced Language, Indonesian
N/A
Year: 2012
Authors: Septina Dian Larasati

Abstract (English):
This paper describes a work on preparing an Indonesian-English Statistical Machine Translation (SMT) System. It includes the creation of Indonesian morphological analyzer, MorphInd, and the composing of an Indonesian-English parallel corpus, IDENTIC. We build an SMT system using the state-of-the-art phrase-based SMT system, MOSES. We show several scenarios where the morphological tool is used to incorporate morphological information in the SMT system trained with the composed parallel corpus.

Abstract (Czech):
(Not yet available. English version repeated)
This paper describes a work on preparing an Indonesian-English Statistical Machine Translation (SMT) System. It includes the creation of Indonesian morphological analyzer, MorphInd, and the composing of an Indonesian-English parallel corpus, IDENTIC. We build an SMT system using the state-of-the-art phrase-based SMT system, MOSES. We show several scenarios where the morphological tool is used to incorporate morphological information in the SMT system trained with the composed parallel corpus.
Tagger Voting for Urdu
Hlasování taggerů pro urdštinu
Year: 2012
Authors: Bushra Jawaid, Ondřej Bojar

Abstract (English):
V článku popisujeme, jak jsme zlepšili značkování slovních druhů pro urdštinu pomocí kombinace dostupných taggerů a dostupných ručně značkovaných dat. V prvním kroku sjednocujeme sady značek užívané v jednotlivých zdrojích. Dále náš vlastní tagger natrénovaný na dostupných datech funguje výrazně lépe než dostupné nástroje. A konečně tento výsledek je možné ještě mírně zlepšit za použití návrhů od ostatních taggerů.

Abstract (Czech):
In this paper, we focus on improving part-of-speech (POS) tagging for Urdu by using existing tools and data for the language. In our experiments, we use Humayoun’s morphological
analyzer, the POS tagging module of an Urdu Shallow Parser and our own SVM Tool tagger trained on CRULP manually annotated data. We convert the output of the taggers
to a common format and more importantly unify their tagsets. On an independent test
set, our tagger outperforms the other tools by far. We gain some further improvement
by implementing a voting strategy that allows us to consider not only our tagger but also
include suggestions by the other tools. The ﬁnal tagger reaches the accuracy of 87.98%.
Khresmoi: Multimodal Multilingual Medical Information Search
Khresmoi: Multimodální a multilingvální hledání medicínských informací
Year: 2012
Authors: Niraj Aswani, Thomas Beckers, Erich Birngruber, Célia Boyer, Andreas Burner, Jakub Bystroň, Khalid Choukri, Sarah Cruchet, Hamish Cunningham, Jan Dědek, Ljiljana Dolamic, René Donner, Sebastian Dungs, Ivan Eggel, Antonio Foncubierta, Norbert Fuhr, Adam Funk, Alba García Seco de Herrera, Arnaud Gaudinat, Georgi Georgiev, Julien Gobeill, Lorraine Goeuriot, Paz Gomez, Mark A. Greenwood, Manfred Gschwandtner, Allan Hanbury, Jan Hajič, Jaroslava Hlaváčová, Markus Holzer, Gareth J.F. Jones, Blanca Jordán, Matthias Jordan, Klemens Kaderk, Franz Kainberger, Liadh Kelly, Sascha Mriewel, Marlene Kritz, Georg Langs, Nolan Lawson, Dimitrios Markonis, Iván Martínez, Vassil Momtchev, Alexandre Masselot, Hélène Mazo, Henning Müller, Pavel Pecina, Konstantin Pentchev, Deyan Peychev, Natalia Pletneva, Diana Pottecher, Angus Roberts, Patrick Ruch, Matthias Samwald, Priscille Schneller, Veronika Stefanov, Miguel Angel Tinte, Zdeňka Urešová, Alejandro Vargas, Dina Vishnyakova

Abstract (English):
Khresmoi is a European Integrated Project developing a multilingual multimodal search and access system for medical and health information and documents. It addresses the challenges of searching through huge amounts of medical data, including general medical information available on the internet, as well as radiology data in hospital archives. It is developing novel semantic search and visual search techniques for the medical domain. At the MIE Village of the
Future, Khresmoi proposes to have two interactive demonstrations of the system under development, as well as an overview oral presentation and potentially some poster presentations.

Abstract (Czech):
Khresmoi je integrační projekt financovaný Evropskou Unií, který se zabývá multilingválním a multimodálním vyhledávání v medicínských datech.
Improved Spelling Error Detection and Correction for Arabic
Vylepšená detekce a korekce pravopisných chyb v arabštině
Year: 2012
Authors: Mohammed Attia, Pavel Pecina, Younes Samih, Khaled Shaalan, Josef van Genabith

Abstract (English):
A spelling error detection and correction application is based on three main components: a
dictionary (or reference word list), an error model and a language model. While most of the
attention in the literature has been directed to the language model, we show how improvements in
any of the three components can lead to significant cumulative improvements in the overall
performance of the system. We develop our dictionary of 9.3 million fully inflected Arabic words
from a morphological transducer and a large corpus, cross validated and manually revised. We
improve the error model by analysing error types and creating an edit distance re-ranker. We also
improve the language model by analysing the level of noise in different sources of data and
selecting the right subset to train the system on. Testing and evaluation experiments show that
our system significantly outperforms Microsoft Word 2010, OpenOffice Ayaspell and Google
Document.

Abstract (Czech):
Systémy pro detekci a korekci pravopisných chyb jsou obvykle založeny na třech komponentách: slovníku, chybovém modelu a jazykovém modelu. Zatímco většina ostatních prací se zaměřuje především na jazykový model, v této práci se ukazujeme, že vylepšení jakékoliv z těchto komponent může vést k významnému zlešení celého systému.
Rule-based Machine Translation between Indonesian and Malaysian
N/A
Year: 2012
Authors: Raymond Hendy Susanto, Septina Dian Larasati, Francis M. Tyers

Abstract (English):
We describe the development of a bidirectional rule-based machine translation system between Indonesian and Malaysian (id-ms), two closely related Austronesian languages natively spoken by approximately 35 million people. The system is based on the re-use of free and publicly available resources, such as the Apertium machine translation platform and Wikipedia articles. We also present our approaches to overcome the data scarcity problems in both languages by exploiting the morphology similarities between the two.

Abstract (Czech):
(Not yet available. English version repeated)
We describe the development of a bidirectional rule-based machine translation system between Indonesian and Malaysian (id-ms), two closely related Austronesian languages natively spoken by approximately 35 million people. The system is based on the re-use of free and publicly available resources, such as the Apertium machine translation platform and Wikipedia articles. We also present our approaches to overcome the data scarcity problems in both languages by exploiting the morphology similarities between the two.}
Korektor – A System for Contextual Spell-checking and Diacritics Completion
Korektor – systém pro kontextovou kontrolu pravopisu a doplňování diakritiky
Year: 2012
Authors: Michal Richter, Pavel Straňák, Alexandr Rosen

Abstract (English):
We present Korektor – a flexible and powerful purely statistical text correction tool for Czech that goes beyond a traditional spell checker. We use a combination of several language models and an error model to offer the best ordering of correction proposals and also to find errors that cannot be detected by simple spell checkers, namely spelling errors that happen to be homographs of existing word forms. Our system works also without any adaptation as a diacritics generator with the best reported results for Czech text. The design of Korektor contains no language-specific parts other than trained statistical models, which makes it highly suitable to be trained for other languages with available resources. The evaluation demonstrates that the system is a state-of-the-art tool for Czech, both as a spell checker and as a diacritics generator. We also show that these functions combine into a potential aid in the error annotation of a learner corpus of Czech.

Abstract (Czech):
Představujeme Korektor – flexibilní statistický nástroj pro opravu českých textů, jehož schopnosti přesahují tradiční nástroje pro kontrolu pravopisu. Korektor využívá kombinace jazykových modelů a chybového modelu jak k tomu, aby setřídil pořadí nabízených náhrad pro neznámé slovo podle pravděpodobnosti výskytu na daném místě v textu, tak také, aby nalezl i překlepy, které se nahodile shodují s existujícím českým slovním tvarem. Prostou náhradou chybového modelu náš pracuje Korektor také jako systém pro doplnění diakritiky („oháčkování textu“) s nejvyšší publikovanou úspěšností. Systém neobsahuje žádné významné jazykově specifické komponenty s výjimkou natrénovaných statistických modelů. Je tedy možné jej snadno natrénovat i pro jiné jazyky. Ukážeme, jakých zlepšení náš systém dosahuje v porovnání se stávajícími českými korektory pravopisu i systémy pro doplnění diakritiky. Ukážeme také, že kombinace těchto schopností pomáhá při anotaci chyb v korpusu češtiny jako druhého jazyka.
Czech and Machine Translation
Čeština a strojový překlad
Year: 2012
Authors: Ondřej Bojar

Abstract (English):
An up-to-date broad study of various approaches of machine translation and translation evaluation techniques, highlighting their advantages and disadvantages. The book is written in Czech and also primarily focuses on aspects of Czech, documenting that Czech is not only interestingly complex but it is also well supplied with linguistic tools and data.

Abstract (Czech):
Knížka přináší aktuální souborný popis problematiky strojového překladu, různých přístupů k němu a jejich výhod a nevýhod a zabírá se i otázkami vyhodnocování kvality překladu. Čeština jako podkladový jazyk potvrzuje svou vynikající roli: pro podchycení českých jazykových jevů je zapotřebí bohaté teorie, přitom je pro ale češtinu dostatek nástrojů a anotovaných i neanotovaných textových dat.
The generation of machine translation
Generace strojového překladu
Year: 2012
Authors: Ondřej Bojar

Abstract (English):
A talk on machine translation and a deliberation on the progress and regress it can lead to.

Abstract (Czech):
Přednáška o strojovém překladu spojená se zamyšlením, k jaké regeneraci či degeneraci díky překladu spějeme.
A brief intro to machine translation
Jak se dělá strojový překlad
Year: 2012
Authors: Ondřej Bojar

Abstract (English):
An introduction to the task of machine translation, current methods and their inherent limitations for those interested in computer science.

Abstract (Czech):
Seznámení zájemců o informatiku s úlohou strojového překladu, současnými metodami a jejich omezeními.
Machine Translation
Strojový překlad
Year: 2012
Authors: Ondřej Bojar

Abstract (English):
This is short article describing the field of machine translation to a broad audience.

Abstract (Czech):
Stručný článek představuje problematiku strojového překladu širšímu publiku.
Can machine translation do the homework for you?
Udělá za vás strojový překlad domácí úkol?
Year: 2012
Authors: Ondřej Bojar

Abstract (English):
An introduction to machine translation for high school students and their teachers.

Abstract (Czech):
Seznámení studentů středních škol a jejich pedagogů s problematikou strojového překladu.
Manually ranked outputs of Czech-Slovak translations
Manuálne ohodnotené česko-slovenské preklady
Year: 2012
Authors: Ondřej Bojar, Petra Galuščáková

Abstract (English):
Data from three sources (part of Acquis, WMT test set and sentences selected from the set of books) translated by 5 machine translation systems (Česílko, Česílko 2, Google Translate and Moses with different settings) from Czech to Slovak and evaluated by three annotators. The translations were manually ordered according to their quality.

Abstract (Czech):
Dáta z troch zdrojov (časť korpusu Acquis, časť testovacej sady z WMT a vety vybrané z kníh) automaticky preložené pomocou piatich systémov (Česílko, Česílko 2, Google Translate a Moses s rôznymi nastaveniami) z češtiny do slovenčiny a ohodnotené troma anotátormi. Preklady boli ručne zoradené podľa ich kvality.
Probes in a Taxonomy of Factored Phrase-Based Models
Sondy v taxonomii faktorových modelů frázového překladu
Year: 2012
Authors: Ondřej Bojar, Bushra Jawaid, Amir Kamran

Abstract (English):
We introduce a taxonomy of factored phrase based
translation scenarios and conduct a
range of experiments in this taxonomy. We
point out several common pitfalls when designing
factored setups. The paper also describes
our WMT12 submissions CU-BOJAR
and CU-POOR-COMB.

Abstract (Czech):
Představujeme taxonomii pro faktorové modely frázového překladu a provádíme sérii experimentů s konfiguracemi z navržené taxonomie. Odhalujeme přitom řadu chyb v návrhu překladových modelů, jichž je vhodné se vyvarovat. Článek slouží rovněž jako popis našich systémů CU-BOJAR a CU-POOR-COMB v soutěži WMT12.