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Depfix: Jak dělat strojový překlad lépe než Google Translate
No full textA popularization talk about Statistical machine translation and Automatic post-editing for the participants of Czech Linguistics Olympics
HamleDT 2.0: Thirty Dependency Treebanks Stanfordized
Get PDFWe present HamleDT 2.0 (HArmonized Multi-LanguagE Dependency Treebank). HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular recently.
We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes. We describe both of the annotation styles, including adjustments that were necessary to make, and provide details about the conversion process. We also discuss the differences between the two styles, evaluating their advantages and disadvantages, and note the effects of the differences on the conversion.
We regard the stanfordization as generally successful, although we admit several shortcomings, especially in the distinction between direct and indirect objects, that have to be addressed in future.
We release part of HamleDT 2.0 freely; we are not allowed to redistribute the whole dataset, but we do provide the conversion pipeline
Findings of the 2014 Workshop on Statistical Machine Translation
No full textThis paper presents the results of the
WMT14 shared tasks, which included a
standard news translation task, a separate
medical translation task, a task for
run-time estimation of machine translation
quality, and a metrics task. This year, 143
machine translation systems from 23 institutions
were submitted to the ten translation
directions in the standard translation
task. An additional 6 anonymized systems
were included, and were then evaluated
both automatically and manually. The
quality estimation task had four subtasks,
with a total of 10 teams, submitting 57 entries
Tolerant BLEU: a Submission to the WMT14 Metrics Task
No full textThis paper describes a machine translation metric submitted to the WMT14 Metrics Task. It is a simple modification of the standard BLEU metric using a monolingual alignment of reference and test sentences. The alignment is computed as a minimum weighted maximum bipartite matching of the translated and the reference sentence words with respect to the relative edit distance of the word prefixes and suffixes. The aligned words are included in the n-gram precision computation with a penalty proportional to the matching distance. The proposed tBLEU metric is designed to be more tolerant to errors in inflection, which usually does not effect the understandability of a sentence, and therefore be more suitable for measuring quality of translation into morphologically richer languages
Multilingual Test Sets for Machine Translation of Search Queries for Cross-Lingual Information Retrieval in the Medical Domain
No full textThis paper presents development and test sets for machine translation of search queries in cross-lingual information retrieval in the medical domain. The data consists of the total of 1,508 real user queries in English translated to Czech, German, and French. We describe the translation and review process involving medical professionals and present a baseline experiment where our data sets are used for tuning and evaluation of a machine translation system
Comparing Czech and English AMRs
Get PDFThis paper compares Czech and English annotation using Abstract Meaning Represantation formalism
Results of the WMT14 Metrics Shared Task
Get PDFThis paper presents the results of the
WMT14 Metrics Shared Task. We asked
participants of this task to score the
outputs of the MT systems involved in
WMT14 Shared Translation Task. We col-
lected scores of 23 metrics from 12 re-
search groups. In addition to that we com-
puted scores of 6 standard metrics (BLEU,
NIST, WER, PER, TER and CDER) as
baselines. The collected scores were eval-
uated in terms of system level correlation
(how well each metric’s scores correlate
with WMT14 official manual ranking of
systems) and in terms of segment level
correlation (how often a metric agrees with
humans in comparing two translations of a
particular sentence)
Machine Translation of Medical Texts in the Khresmoi Project
Get PDFThe WMT 2014 Medical Translation Task poses an interesting challenge for Machine Translation
(MT). In the standard translation task, the end application is the translation itself. In this task, the MT system is considered a part of a larger system for cross-lingual information retrieval (IR)
CLARA: A New Generation of Researchers in Common Language Resources and Their Applications
No full textCLARA (Common Language Resources and Their Applications) is a Marie Curie Initial Training Network which ran from 2009 until 2014 with the aim of providing researcher training in crucial areas related to language resources and infrastructure. The scope of the project was broad and included infrastructure design, lexical semantic modeling, domain modeling, multimedia and multimodal communication, applications, and parsing technologies and grammar models. An international consortium of 9 partners and 12 associate partners employed researchers in 19 new positions and organized a training program consisting of 10 thematic courses and summer/winter schools. The project has resulted in new theoretical insights as well as new resources and tools. Most importantly, the project
has trained a new generation of researchers who can perform advanced research and development in language resources and technologies
English to Urdu Statistical Machine Translation: Establishing a Baseline
No full textThe aim of this paper is to categorize and present the existence of resources for English-
to-Urdu machine translation (MT) and to establish an empirical baseline for this task.
By doing so, we hope to set up a common ground for MT research with Urdu to allow
for a congruent progress in this field. We build baseline phrase-based MT (PBMT) and
hierarchical MT systems and report the results on 3 official independent test sets. On all
test sets, hierarchial MT significantly outperformed PBMT. The highest single-reference
BLEU score is achieved by the hierarchical system and reaches 21.58% but this figure
depends on the randomly selected test set. Our manual evaluation of 175 sentences
suggests that in 45% of sentences, the hierarchical MT is ranked better than the PBMT
output compared to 21% of sentences where PBMT wins, the rest being equal